Biomarkers of immune dysfunction in response to chronic stress, methods of use and diagnostic kits

ABSTRACT

Diagnostic biomarkers for diagnosing immune suppression/dysfunction. The diagnostic biomarkers are genes and/or transcripts that are up or down regulated compared to normal expression when a subject has been stressed either mentally and/or physically. The invention also relates to a method of detecting comprised or suppressed immune response in a subject by comparing certain diagnostic biomarkers in the subject to a control set of diagnostic biomarkers.

This application claims priority and is a continuation application ofPCT application no. PCT/US2013/000097 filed Mar. 28, 2013, pending,which claims priority of U.S. provisional application No. 61/687,731filed Apr. 28, 2012.

GOVERNMENT INTEREST

The invention described herein may be manufactured, used and licensed byor for the U.S. Government.

BACKGROUND OF THE INVENTION 1 Field of the Invention

The present invention relates to diagnostic biomarkers of immunesuppression/dysfunction. The diagnostic biomarkers may be used toevaluate the capability of immune cells in subjects, and screen subjectsfor immune suppression/dysfunction in response to stress and/or pathogenexposure.

The present invention further relates to diagnostic biomarkers suitablefor diagnosing Staphylococcus Enterotoxin B (SEB) exposure in a subject,and methods of using the same. These diagnostic biomarkers are suitablefor diagnosing SEB exposure in the presence of comprised immune responseor stress.

SUMMARY OF THE INVENTION

Diagnostic biomarkers for diagnosing immune suppression/dysfunction. Thediagnostic biomarkers are transcripts that are up or down regulatedcompared to normal expression when a subject has been stressed eithermentally and/or physically. The invention also relates to a method ofdetecting comprised or suppressed immune response in a subject bycomparing certain diagnostic biomarkers in the subject to a control setof diagnostic biomarkers.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A is a graph showing comparisons of before and after training ofweights, temperatures and blood pressures of cadets;

FIG. 1B) is a graph showing differential and complete leukocyte countsof trainees before and after training including complete anddifferential blood counts for pre- and post-Training subjects thatinclude red blood cells, white blood cells, neutrophils and lymphocytes;monocytes, eosophils and basophils;

FIG. 1C is a graph showing differential and complete leukocyte counts oftrainees before and after training that include complete anddifferential blood counts for pre- and post-Training subjects includedmonocytes, eosophils and basophils;

FIG. 2A is a table showing the analysis of differentially expressedgenes in leukocytes of Ranger Trainees before and after Training;

FIG. 2B is a heat map that shows Hierarchical clustering of 288 genesthat passed Welch's t-test with FDR correction (q<0.001) and hadexpression alteration of a 1.5 fold with each lane showing the 288 genesand their leukocyte expression level for each subject before (leftpanel) or after (right panel) training in comparison to human universalRNA;

FIGS. 3A-E are graphs showing correlation of real time PCR arrays withthose from cDNA and oligonucleotide microarrays;

FIG. 4A is a graph showing correlation of Real time QPCR and cDNAmicroarray analyses;

FIG. 4B) is a graph showing ELISA determination of plasma concentrationsof proteins, and comparison with level of their transcripts frommicroarrays data;

FIG. 5A is a heat map of expression patterns of immune response genes inleukocytes in-vitro exposed to SEB;

FIG. 5B is a heat map of predicted and experimentally observed targetsof RASP-regulated microRNAs;

FIG. 5C is a sample PCA of differentially regulated microRNAs thatpassed Welch's Test (p<0.25) and 1.3 fold change cut off;

FIG. 5D is a map of regulatory interaction among stress-induced miRs,important transcription factors (NFkB1, NR3Ca, SATB1), inflammatorycytokines and antigen presenting molecules;

FIG. 5E is a map showing seven stress-suppressed miRs targeting 48 mRNAsamong differentially regulated mRNAs that passed q<0.001 and 1.5 foldchange;

FIG. 6 is a graph showing predicted targets of miR-155 and let-7ffamilies;

FIG. 7A is a map of transcription factors predicted to be inhibited bybattlefield stressors and their targets among stress-affected genes;

FIG. 7B is a map showing transcription factors targeting RT-PCR assayedand differentially regulated genes;

FIG. 8 is a map of functional network of differentially expressed genesconnected by their sub-functions in the immune system;

FIG. 9A is a map showing immune response transcripts involved in patternrecognition, viral, antibacterial and effector (humoral) responses;

FIG. 9B is a diagram showing roles of stress down regulated genes in thecellular pathways of immune response;

FIG. 9C is a diagram of action of secreted cytokines on otherleukocytes;

FIG. 10A is a diagram showing antigen presentation pathways;

FIG. 10B is a diagram showing expression pattern of genes important forimmunological synapse formation;

FIG. 11 is a diagram showing Canonical pathways significantly associatedwith stress regulated genes that passed Welch's t-test and FDRcorrection (p<=0.001) and 1.5 fold change;

FIG. 12 is a graph showing relative contribution (rank) of genes inclassifying (predicting) control and stress groups of Ranger samplesranked using the Nearest shrunken centroid prediction approach;

FIG. 13A is a graph showing stress specific genes differentiating stressfrom SEB, dengue virus and Yersinia pestis (plague) infections;

FIG. 13B is also a graph showing stress specific genes differentiatingstress from SEB, dengue virus and Yersinia pestis (plague) infections;

FIG. 14 is a graph showing misclassification error rate vs thresholdvalue; and

FIG. 15 is a graph showing cross-validation of the prediction analysisof the invention.

DETAILED DESCRIPTION

Previous studies suggest that excessive or prolonged stress impairsprotective immunity towards infection leading to increase susceptibilityto illness. Comprehensive molecular explanations of the host'sphysiological stress response and the results of failed adaptation overtime offer the potential to identify the debilitating pathophysiologicconsequence of severe stress on health. More importantly, molecularapproaches offer the opportunity to implement clinical strategies todifferentiate immune impaired individuals from their normalcounterparts.

Applicants examined the effects of long-term battlefield-like stressorsof U.S. Army Ranger Training on genome wide expression profiles forbiomarker identification of prolonged severe, stress-induced,compromised immune response. Applicants identified 59 differentiallyregulated transcripts using comparative Welch's T-test along withBonferroni correction (q<0.01) followed by 3-fold change. These 59differentially regulated transcripts are identified at Table 3 herein.Among the 59 differentially regulated transcripts identified, 48 weredown regulated and 11 were up regulated. Most of the down-regulatedtranscripts were directly involved in protective immunity.

Differentially regulated transcripts identified and their cognatepathways were confirmed using quantitative real-time PCR arrays. Antigenpreparation and presentation, chemotaxis, inflammation, and activationof leukocytes were among overrepresented immune response processes thatwere significantly associated with suppressed transcripts.Differentially regulated transcripts identified or genes from theircorresponding pathway can serve as diagnostic biomarkers todifferentiate/identify individuals with stress-induced immunesuppression. cDNAs of some of these transcripts can be electrochemicallytethered in the wells of micro- or nano-chips for quick diagnosispurpose.

Diagnostic biomarkers within the scope of the present invention for usein identifying or screening individuals for immunesuppression/dysfunction include five (5) or more, seven (7) or more, orten (10) or more of the 59 differentially regulated transcriptsidentified herein or genes from their corresponding pathway. For examplepurposes, Applicants provide herein a subset of 14 of the 59 transcriptsthat can be used as a single batch of biomarkers (see Table 3A and 3B).The five (5) or more, seven (7) or more, ten (10) or more or twenty (20)or more of the differentially regulated transcripts or genes from theircorresponding pathway may, for example, be selected from these. It isunderstood to one of ordinary skill in the art that there may beadditional biomarkers, not yet identified, that can be used to screenindividuals for immune suppression/dysfunction. This invention is notlimited to the 59 biomarkers listed in Table 3.

These diagnostic biomarkers would be useful to diagnose immunesuppression/dysfunction in a subject due to stress. The presentinvention further relates to diagnostic kits for use in screening immunefunction of a subject, where the kit employs the diagnostic biomarkersidentified herein.

Applicants further conducted studies on the effect of stress on apatient's ability to respond to other pathogens. More specifically,Applicants studied the effect of Staphylococcus Enterotoxin B (SEB) onhost response gene expression profiles, and identified genes that showedconsistent differential expression towards SEB whether or not the hosthad been exposed to stress. These transcripts or genes from theircorresponding pathway were SEB-specific (independent of the physiologicand pathologic status of the host), and may serve as diagnostic markersof SEB exposure.

Therefore, this invention proposes a simple test to identify thecapability of immune cells to respond to pathogenic agents in militarypersonnel. This biomarker profile would allow for a semi-quantitativemethod to evaluate the immune system in terms of gene expression.

Transcriptomic Characterization of Immune Suppression fromBattlefield-Like Stress

This invention identifies changes in transcriptome of human due tobattlefield-like stress. Thorough understanding of stress reactions islikely to produce better strategies to manage stress, and improvehealth¹. Stress modulates gene expression, behavior, metabolism andimmune function²⁻⁵. Chronic physiological and psychological stresses aremajor contributors of stress-induced suppression of protective immunity.For example, chronic stress impairs lymphocyte proliferation,vaccination efficacy⁶⁻⁹, NK cell activity, resistance to bacterial andviral infection¹⁰, and increases risk of cancer¹¹.

Yet, comprehensive descriptions of molecular responses to stress areneeded to fully understand modulated networks and pathways, and hence toreduce and prevent pathophysiologic effects of intense and prolongedstresses.

Here we report gene expression changes occurring in leukocytes collectedfrom Army Ranger Cadets before and after eight-week Ranger Training.Ranger cadets are exposed to different and extreme physical andpsychological stressors of Ranger Training Course, which is designed toemulate extreme battlefield scenarios: sleep deprivation, calorierestriction, strenuous physical activity, and survival emotionalstresses—pushing cadets to their physical and psychological limits. TheRanger population provides a rare opportunity to study intense chronicbattlefield-like stress, and to contribute to the understanding ofintense chronic stress in general. Ranger Training has been shown toimpair cognitive function, cause significant declines in3,5,3′-triiodothyroxine and testosterone, and increase cortisol andcholestero^(12; 13).

Transcriptomic alterations, in this study, were assayed using cDNAmicroarrays. Results were corroborated with oligonucleotide, microRNAs,and real-time QPCR arrays, and were confirmed using Quantitative RT-PCRand ELISA. Analyses of functional and regulatory pathways ofdifferentially altered transcripts revealed suppression of immuneprocesses due to battlefield-like stress. Some of stress inducedmicroRNAs, and a number of stress inhibited transcription factors werefound to regulate or be modulated by many compromised immune responsetranscripts. Suppressed immune response genes remained suppressed evenafter exposure of post-stress leukocytes to mitogenic toxin, SEB. Thisimpaired activation is a clear indicator of anergy, and compromisedprotective immunity.

Results

Ranger Trainees experience an average daily calorie deficit of 1000-1200kcal, restricted and random sleep of less than 4 hours per day,strenuous and exhaustive physical toiling and emotional survivalstressors. Five of the initial fifteen Trainees enrolled in our studywere replaced with five others due to attrition (to maintain 15 studysubjects at both time points). All study subjects had complete anddifferential blood counts performed, and were observed for infectionsand injuries. By the end of training, Trainees showed significantaverage weight loss, decreased body mass index and diastolic bloodpressure, and significant increase in average body temperature andsystolic blood pressure (FIG. 1A); and they showed metabolite patternstypical of severe stress. The vertical lines show the ranges of cellcounts. (Normal Ranges are WBC 5-12×10³/mm³; NEU 2-8×10³/mm³; LYM1-5×10³/mm³; MON 0.1-1×10³/mm³; EOS 0.0-0.4×10³/mm³; BAS0.0-0.2×10³/mm³.)

Differential and complete blood counts showed small but significantdifferences between pre- and post-Training cells, yet all were withinnormal ranges (FIGS. 1B and 1C). To normalize for cell countdifferences, equal number of pre- and post-Training leukocytes were usedfor isolation of RNA, and equal amounts of isolated RNAs were used formicroarrays, and RT-QPCR assays.

As shown in FIGS. 1B-1C, differential and complete leukocyte counts ofsoldiers before and after RASP are presented. Differential and completeblood counts for pre- and post[RASP subjects included red blood cells(RBC), white blood cells (WBC), neutrophils (NEU), lymphocytes (LYM),monocytes (MON), eosinophils (EOS) and baseophils, (BAS). Usingcomparative t-test, only RBC (P<0.006) and BAS (p<0.02) weresignificantly changed (reduced) after RASP. The ranges of cell countsincluding RBC and BAS (shown by the vertical lines) were within normalranges. Normal ranges are WBC5-12×10³ mm⁻³; NEU 2-8×10³ mm⁻³; LYM1-5×10³ mm⁻³; MON 0.1-1×10³ mm⁻³; EOS 0.0-0.4×10³ mm⁻³; BAS 0.0-0.2×10³mm⁻³.

Transcriptome Profiling of Pre- and Post-Training Leucocytes

We used three transcriptome profiling techniques to cross-validate ourfindings: cDNA and oligonucleotide microarrays, and quantitative realtime PCR arrays. Expression profiles were done on total RNAs isolatedusing two different methods: Trizol (Invitrogen. Inc) and PAXgene,(Qiagen.Inc).

cDNA Microarrays Analyses

To analyze gene expression profiles of leukocytes of Ranger Cadetscollected before and after eight-week Training, we used custom cDNAmicroarrays that contained ˜10 000 well-characterized cDNA probes of 500to 700 base pairs representing ˜9 000 unique human gene targets. Welch's(unpaired unequal variance) t-test along with false discovery rate (FDR)correction was used on normalized expression data to identify 1 983transcripts that were significantly changed (q≦0.05), with 1 396 showing≧1.5 fold change in expression level between pre- and post-Trainingsamples (Table 4). Among 1 396 differentially regulated genes, 288 genesFIG. 2B were significantly changed at q≦0.001, and 87 of these weredifferentially regulated by >3-fold change. Of these 87 genes, 72 weredown-regulated, and 68 of 72 genes have direct role in immune response,including 23 of the 25 most down-regulated genes. These results stronglysuggest that Ranger Training stressors suppress the immune response, andthis finding was corroborated by functional and pathway enrichments.

Functional enrichments of significantly regulated genes using bothhypergeometric test (FDR correction, q≦0.05), and Fishers exact testidentified the immune system as the most affected biological process.Apoptosis, stress response, response to wounding, metabolism, hormonereceptor signaling (peptide and steroid), cell cycle and unfoldedprotein response signaling were also significantly associated withaltered transcripts. Yet, immune system process was most significantlyover-represented (q<1.7E-16), and was associated with 177 differentiallyregulated genes. Of the 177 genes, 151 were down-regulated, and 26 wereup-regulated. Further functional enrichment of the 151 genes indicatedthat these genes were significantly associated with microbialrecognition, inflammation, chemotaxis, antigen presentation, andactivation of lymphocytes, mast cells and macrophages (Tables 1). The 26Up-regulated immune response genes were associated with response tosteroid hormone stimulus, regulation of leukocyte activation, complementactivation, negative regulation gene expression, and negative regulationof phosphorylation (Table 1).

TABLE 1 Functions significantly associated with differentially regulatedimmune response genes that passed Welch's t-test and FDR correction (q <0.05 and showed >1.3 fold change in post RASP leukocyted compared withpre-RASP leukocytes. GO-ID Function Gene symbol (note these ar symbolsand not sequences) Functions of down-regulated immune response genes45321 leukocyte activation MICA, CD8A, CD8B, ELF4, TLR4, ADA, CD74,CD93, CD2, FCER1G, CD4, SYK, IL4, KLF6, PTPRC, CD3D, IL8, CD3E, RELB,SLAMF7, CD40, LAT, LCK, CD79A, LCP2 6954 inflammatory response CXCL1,ITGAL, TNF, TLR2, NFKB1, ITGB2, TLR4, CCL5, CD97, CCL20, KRT1, IL1B,IL1A, CEBPB, IL8, IL1RN, GRO3, CD40, CCL18, CD180, C8G, SCYA7, CCL13,CCR7, CYBB, CCR5, CRH, CD14 19882 antigen processing and HLA-DQB1, MICA,CD8A, HLA-DRB1, RELB, HLA-C, FCGRT, HLA-B, presentation HLA-G, CD74,B2M, FCER1G, HLA-DPA1, HLA-DPB1, HLA-DOB, AP3B1, HLA-DRA 46649lymphocyte activation IL4, PTPRC, KLF6, MICA, CD3D, CD8A, ELF4, CD3E,CD8B, RELB, CD40, SLAMF7, CD74, ADA, LCK, CD2, CD4, CD79A, SYK 30097hemopoiesis IL4, PTPRC, KLF6, CD3D, LYN, HCLS1, RELB, IFI16, MYH9,CD164, CD74, LCK, CD4, SPIB, CD79A, MYST1, SYK, MYST3 52033pathogen-associated molecular PF4, CHIT1, TLR2, TLR4, SCYA7, CD14,PF4V1, CLP1, TICAM1, pattern recognition FPRL1, FPR1 6935 chemotaxisIL4, CXCL1, C5AR1, IL8, GRO3, ITGB2, PF4, CCL5, CCL18, SCYB5, SCYA7,CCL13, CCR7, CCR5, PPBP, CCL20, IL1B, FCER1G, SYK, 42110 T- cellactivation PTPRC, MICA, CD3D, CD8A, CD3E, CD8B, ELF4, RELB, CD74, ADA,LCK, CD2, CD4, SYK 2274 myeloid leukocyte activation LAT, IL8, CD93,RELB, FCER1G, TLR4, LCP2 50778 positive regulation of immune PTPRC,MICA, SLK, FYN, KRT1, TLR2, FCER1G, CD79A, C8G, SYK response 6959humoral immune response PSMB10, CD83, ST6GAL1, TNF, HLXB9, POU2F2, KRT1,AIRE, C8G 1934 positive regulation of TNF, CCND3, LYN, HCLS1, IL1B, CD4,SYK phosphorylation 45087 innate immune response CYBB, IL1R1, SARM1,CLP1, KRT1, TLR2, TLR4, SLAMF7, CD180, C8G 2252 immune effector processPTPRC, LAT, MICA, FCN2, KRT1, FCER1G, SLAMF7, CD74, C8G 30593 neutrophilchemotaxis IL8, FCER1G, IL1B, ITGB2, SYK 7229 integrin- signaling LAT,ITGAL, ITGAX, ITGB2, MYH9, ITGAM, SYK 45058 T- cell selection CD3D, CD4,CD74, SYK 1816 cytokine production IL4, CD4, ISGF3G, CD226, LCP2 6909phagocytosis CD93, FCN2, CLP1, FCER1G, CD14 2460 somatic recombinationfor IL4, RELB, FCER1G, TLR4, CD74, C8G adaptive response Functionsassociated with up-regulated immune response genes 48545 response tosteroid hormones CEBPA, CAV1, HMGB2, PRKACA, CD24 42326 negativeregulation of CAV1, PRKACA, INHA phosphorylation 6956 complementactivation C4B, C3, C2 10817 regulation of hormone levels DHRS2, ACE,FKBP1B 43434 response to peptide hormones HHEX, PRKDC, PRKACA 2762negative regulation of myeloid FSTL3, INHA leukocyte differentiation32088 negative regulation of NFkB POP1, SIVA activity 51384 response toglucocorticoids CEBPA, CAV1, PRKACA 16481 negative regulation of CEBPA,HHEX, CAV1, HMGB2, FST, transcription HELLS

Oligonucleotide Microarrays

Gene expression alterations in leukocytes of Rangers before and afterTraining were also analyzed using PAXgene RNA isolation andoligonucleotide microarrays representing 24 650 human gene probes. Thisdifferent RNA isolation procedure and microarray assay again showed thatthe immune system was most significantly affected process. Normalizedexpression levels were analyzed using Welch's t-test (p<0.05, withoutmultiple correction), and fold change filter (>=1.5 fold). Among 1570genes (that passed these filters), 104 genes were associated with theimmune response processes including microbial recognition, chemotaxis,inflammation, antigen presentation, and T-cell, B-cell and NK-cellactivations (FIGS. 3A-E & Table 5).

Real Time Quantitative PCR Array

We used real time quantitative PCR (QPCR) arrays to confirm differentialexpression of genes identified by cDNA and oligonucleotide microarrays,and to survey additional immune related genes. Assay results of PCRarrays that contained more than 160 genes in antigen presentation andNFkB signaling pathways (RT² Profiler™ PCR Arrays, SABioscience, MD)verified down-regulation of 116 immune response genes, consistent withmicroarray data (Tables 3A, 3B and 4). The vast majority of the genesimportant for microbial pattern recognition, inflammation, antigenpresentation, T-cell activation and transcription factors related toimmune response were suppressed across cDNA, oligonucleotide and PCRarrays (FIGS. 3A and 3B)

Referring to FIGS. 3A-E, genes are shown that are associated withpattern recognition receptors (FIG. 3A); inflammatory response (to scalethe graph, fold changes of −15.2 and −23.8, labeled * and **,respectively, were assigned a values of ˜5 and 6, respectively (FIG.3B); antigen preparation and presentation (*fold change: −12.3; assignedvalue ˜−5 for scaling the graph) (Fig. C); transcription factors (*foldchange: −12.6; **fold change: −12.3; ***fold change: −14; these wereadjusted to around −5 for scaling the graph) (FIG. 3D); T-cellactivation, differentiation and proliferations. Expression profiles ofgenes shown in pannels A-E were assayed using SABiosciences RT²Profiler™(PAHS 406 and PHAS 25) PCR Arrays, cDNA microarrays, and oligonucleotidemicroarrays (FIG. 3E). Total RNA samples were isolated using Trizolreagents for cDNA microarray analysis, and total RNA samples used forPCR and oligonucleotide arrays were isolated from blood samplescollected in PAXgene tubes. (Note: PCR arrays were carried out onsubjects participated throughout our study, and fold changes for thesefigures were calculated on data from both round subjects).

Real Time Quantitative PCR

Additional quantitative real-time PCR assays were carried out usingspecific primer pairs to confirm 10 representative genes among 1396significantly altered genes shows number of genes that passed Welch'st-test at different q-values (FDR corrected p-values) and Fold Changecut-offs) (FIG. 2A)(Table 2). Real-time QPCR Assayed and confirmed genesincluded IL1B, IL2RB, CD 14, HLA-G, RAP1A, AQP9, ALB, CSPG4, CDC2, A2M,and GAGE2. Individual real-time QPCR results confirmed and validatedthese differentially expressed genes identified by cDNA arrays (FIG.4A).

FIG. 4A shows Real time PCR reactions for each gene were carried outwith three or more replicates. The microarray data were from Trizol RNAisolation and cDNA microarrays (*p-values<10⁻⁵, **p-values<0.0002,***p-value<0.02). The p-values given here were taken from the microarrayanalyses obtained after FDR correction.

Genes Associated with Microbial Recognition

Genes associated with microbial pattern recognition were significantlysuppressed in post-Training leukocytes (Table 5, & Tables 1 & FIG. 5D).These genes include Toll-like receptors (TLR 2, 3, and 4), CD14, CD93,chitinase 1 (CHIT1), formyl peptide receptor 1 (FPR1), formyl peptidereceptor like 1 (FPRL1), dicer1 (DICER1), cleavage and polyadenylationfactor I subunit (CLP1), platelet factor 4 (PF4), platelet factor 4variant 1 (PF4V1), toll-like receptor adaptor molecule 1 (TICAM1), andmyeloid differentiation primary response gene 88 (MYD88). TLR6 wasdown-regulated but it did not pass the FDR correction filter.

CD 14, along with TLR4/TLR4 and TLR2/TLR6, recognize lipopolysaccharidesand peptideoglycans, respectively. TLR3, CLP1 and DICER1 bind to doublestranded viral RNAs. TLR9 and CD93 recognize unmethylated CpGdinucleotides of bacterial DNA, and patterns of apoptotic cells,respectively. FPR1 and FPRL1 bind bacterial N-terminal formyl-methioninepeptides. CHIT1 recognizes fungal and pathogens with chitin patterns.PF4 and PF4V1 recognize patterns of plasmodium and tumor cells. TICAM1and MYD88 are important cytosolic adaptor molecules of microbial patternrecognitions. Transcripts of these genes were down-regulated suggestinga compromised innate immune response with regard to microbialrecognition.

Genes Associated with Chemotaxis and Inflammation

Stress suppressed transcripts associated with chemotaxis andinflammation included interleukins (IL 1A, IL1B, IL4, IL8), interleukinreceptors (IL1R1, IL1RN, IL2RB, IL10RA), chemokine (C-X-C motif) ligands(CXCL 1), chemokine (C-C motif) ligands (CCL13, CCL18, CCL20), tumornecrosis factor alpha (TNFα), TNF receptor super-family members 1B, 10Band 10C (TNFRSF1B, TNFRSF10B and TNFRSF10C), TNF superfamily members 3,8, (LTB, TNFSF8), complement component 8 gamma (C8G), cytochrome b-245beta (CYBB), CD97 and interferon gamma receptor (IFNGR2) (Tables 1 & 5).

Genes Associated with Activation of Myeloid Leukocytes

Tables 1 & 5 show suppressed transcripts associated with activation ofmast cells and macrophages. These included toll-like receptors (TLR4),TNF, LAT, lymphocyte cytosolic protein 2 (LCP2), SYK, CD93, and IL4RELB. Suppressed genes associated with inflammatory responses (ILL CD14,INFGR1) were also significantly associated with activation of myeloidcells. Differentiations of myeloid leukocytes were significantlyassociated with interferon gamma inducible proteins 16 and 30 (IFI16),myosin heavy chain 9 (MYH9), IL4, Spi-B transcription factor (SPIB),NFkB3, MYST histone acetyltransferases (MYST1 and 3), TNF, PF4,hematopoitic cell-specific lyn substrate 1 (HCLS1), V-yes-1 Yamaguchisarcoma viral related oncogene homolog (LYN) and V-maf(musculoaponeurotic fibrosarcoma) oncogene homolog b (MAFB).Down-regulation of hemopoietic transcription factors (MAFB and HCLS1)and CSF1R may indicate less viability of myeloid cells to expand or toreplenish. Suppression of mRNAs of these genes suggests poor activation,differentiation and proliferation of myeloid leukocytes in response toinfection, and hence poor innate and adaptive immune responses.

Genes Associated with Antigen Presentation

Genes associated with antigen preparation encompass MHC classes (I &II), CD1s, B-cell co-receptors and integrins (Tables 1 and 5).Transcripts of MHC class I (HLA-B, HLA-C, HLA-G, beta-2-microglobulin(B2M)), MHC class II (HLA-DRB1, HLA-DRA, HLA-DPA1, HLA-DPB1, HLA-DQA1,HLA-DQB1, CD74, HLA-DOB), B-cell co-receptors (CD79A, CD79B), Ig heavyconstant gamma 1 (IGHG1), Ig heavy constant alpha 1 (IGHA1), MHC class Ipolypeptide related sequence A (MICA), adaptor-related protein complex 3beta1 (AP3B1), intercellular adhesion molecules 1, 2 and 3 (ICAM1,ICAM2, ICAM3) were down-regulated implying poor antigen preparation andpresentation, and hence impaired adaptive immune response.

Genes Associated with Activation of Lymphocytes

Suppressed transcripts associated with T-cell activation,differentiation and proliferation included TCR co-receptors (CD4, CD8α,CD8β, CD3ε, CD3δ, CD247), linker for activation of T cells (LAT), TCRsignaling molecules [protein kinase c theta (PRKCQ), protein tyrosinephosphatase receptor type C (PTPRC), C-SRC tyrosine kinase (CSK), spleentyrosine kinase (SYK) lymphocyte specific protein tyrosine kinase(LCK)], integrins CD2, CD44, integrin alpha L, M and X (ITGAL, ITGAM,ITGAX), and cyclin D3 (CCND3) (Tables 1 & 5).

Interleukin 4, SYK, PRKCD, CD40, PTPRC, cyclin-dependent kinaseinhibitor 1A (CDKN1A), Kruppel-like factor 6 (KLF6), SLAM family member7 (SLAMF7), and killer cell Ig-like receptor three domains longcytoplasmic tail1 (KIR3DL1) were significantly associated withactivation, differentiation and proliferation of B-cells, and NK-cells(Tables 1 & 5).

Transcription Factors Associated with Immune Responses

Transcription factors that are important regulators of immune responsegenes were down-regulated. Suppressed factors included nuclear factorkappa B family (NFkB1, NFkB2, RELA, RELB), interferon regulatory factors1, 5, 7, 8 (IRF1, IRF5, IRF7 and IRF8), signal transducer and activatorof transcription (STAT2, STAT6), and SP transcription factors (SP1,SP140) (Tables 1 & 5). In addition, transcription factors GA bindingprotein alpha (GABPA), POU class 2 homeobox 2 (POU2F2), p53 (TP53), p53binding protein 1 (TP53BP1), early growth response 2 (EGR2), splicingfactor 1 (SF1), and hypoxia inducible factor 3 and alpha subunit (HIF3A)were down-regulated. Up-regulated transcription factors includedhepatocyte nuclear factor 4 alpha (HNF4A) hepatic leukemia factor (HLF),sterol regulatory element binding transcription factor 2 (SREBF2)transcription factor AP-2 alpha (TFAP2A), transcription factor 7-like 2(TCF7L2) and NF-kappa-B inhibitor-like 2 (NFKBIL2) (Tables 1 & 5).

ELISA Assays of Plasma Proteins

Plasma concentrations of insulin-like growth hormones 1 and 2 (IGF1 andIGF2), prolactin (PRL), tumor necrosis factor alpha (TNF), andenzymatic-activity of superoxide dismutase 1 (SOD 1) were determined byELISA to examine gene expression alterations at the protein level.Relative quantities of proteins, and levels of transcripts profiled bycDNA and oligonucleotide microarrays were compared (FIG. 4B). ReducedIGF1 has been shown to be a biomarker of negative energy balance underconditions of multiple Ranger Training stressors¹², and IGF1 transcriptin leukocytes and protein in plasma are reduced after Training. Plasmaconcentration of PRL was up-regulated while transcriptome profilingshowed down-regulation by microarray analyses, suggesting differentialregulation of prolactin at transcription and translation levels.

FIG. 4B shows plasma concentrations of prolactin (PRL), insulin-likegrowth factors I and II, tumor necrosis factor alpha (TNF α) andenzymatic activity of superoxide dismutase 1 (SOD 1) were assayed usingnine biological replicates and three experimental replicate samplescorresponding to each biological replicate for each of these proteins.The IGF-I depletion is consistent with other studies that measured itsplasma concentration on similar subjects¹³ (*p-values<0.003,**p-values<0.04, ***p-value <0.0002).

Response of Leukocytes to Ex Vivo Treatment of StaphylococcalEnterotoxin B

Staphylococcus enterotoxin B (SEB) is a superantigen, and a potent Tcell activator known to induce proinflammatory cytokine release invitro¹⁴. Leukocytes of Ranger Trainees collected before and afterTraining were challenged ex vivo with SEB and immune responsetranscripts were analysed. In pre-Training leukocytes, SEB toxin inducedmajority of immune response genes (FIG. 5A). However, in post-Trainingleukocytes, stressed suppressed immune response genes showed no sign ofre-activation even after ex vivo exposure to SEB (FIG. 5A). Rather SEBseemed to further suppress expression of many of these transcripts.Impaired response of post-Training leukocytes to SEB is consistent withsuppression of immune response pathways and networks revealed bytranscriptome analyses.

In FIG. 5A, expression of immune response genes in leukocytes exposed exvivo to SEB is shown. Leukocytes isolated from whole blood were treatedwith SEB (˜10⁶ cells ml⁻¹ in RPMI 1640 and 10% human AB serum at a finalconcentration of 100 ng ml⁻¹ SEB). Total RNA was isolated using Trizoland expression levels were profiled using cDNA microarrays. Shown hereare the 151 RASP-suppressed immune response genes that passed Welch'stest and FDR correction (q<0.05). (a) Lanes left to right: pre-RASPsamples not exposed to SEB (control), pre-RASP samples exposed to SEB,post-RASP samples not treated with SEB, post-RASP samples exposed toSEB. For comparative visualization purpose, expression values of theother groups were transformed against the Pre-RASP control samples(black lane). Heat map of the same data without transformation is givenin the supplement. (b) Expression values in SEB exposed leukocytes (inboth the pre- and post-RASP conditions) were compared with thecorresponding SEB untreated groups (pre-RASP control and post-RASPstressed groups). (c) Heat map of 151 immune response genes in SEBtreated groups (in both pre- and post-RASP leukocytes) clustered aftersubtraction of the corresponding baseline responses (cluster aftersubtraction of their expressions in the corresponding untreated groupsshown in lane (b). Lane c clearly shows pour response of post-RASPleukocytes towards SEB exposure compared with pre-RASP leukocytes.

MicroRNA Arrays

Differentially regulated microRNAs (miRs) in pre- and post-Trainingsamples were assayed using Agilent's human microRNA chip containing ˜15000 probes representing 961 unique miRs. Comparison of 535 miRs (thatpassed normalization and flag filters) using Welch's t-test at p<0.1with a 1.3 fold change cutoff gave 57 miRs (FIG. 5C). MicroRNA targetscan was used to identify high-prediction and experimentally proventargets of these differentially regulated miRs. Among up-regulated miRs,hsa-miR-155 (p<0.08) and hsa-let-7f (p<0.1), were shown to target manysuppressed transcripts, including transcription regulators of genesimportant for dendritic cell maturation and glucocorticoid receptorsignaling. Expression of miR-155 was suppressed in pre-Training samplesexposed to SEB, but it was induced in post-Training samples treated withSEB (FIG. 6). Other stress-induced miRs were predicted to haveregulatory connection with stress-affected inflammatory cytokines,antigen-presenting molecules, and transcription regulators of genesinvolved in immune response (FIG. 5D). Stress-suppressed miRs—miR-662,miR-647, miR-876-5P, miR-631, miR-1296, miR-615-3P, and miR-605—have anumber of regulation targets among stress-regulated genes involved inNFkB activation pathways (FIG. 5E). In FIG. 5E enriched pathways: IL-7and IL-8 signalings, and NFkB activation pathways are shown. No targetswere identified for two highly suppressed miRs, miR-1910 and 1909*.

FIG. 6 shows predicted targets of miR-155 and hsa-let 7f families. InFIG. 6, expression levels of hsa-miR-155 and hsa-let-7f in pre-RASP(control), post-RASP (stressed) and pre-RASP exposed to SEB, andpost-RASP exposed to SEB groups. Sequences of mature miR-155 and let-7fare also shown.

See also FIG. 5B for predicted and experimentally observed targets ofRASP-regulated micro RNAs. 57 microRNAs passed Welch's T-test (P<0.1)and 1.3 fold change. Most (46 of 57) miRs were downregulated, and 11miRs were upregulated in post-RASP leukocytes.

Expression Data Based Prediction of Transcription Factors and TargetGenes

Computational & data analyses tools, and databases (see Materials andMethods) were used for empirical and predictive association oftranscription factors (TFs) and their regulatory targets amongstress-altered genes. Activated or inhibited TFs, common regulatorysites of target genes, and prediction z-scores of identified TFs werecomputed based on 1369 differentially regulated genes obtained from cDNAarray data (Table 2). TFs at the top of stress-inhibited list (IRF7,RELA, NFkB1, RELB, CREB1, IRF1, HMGB1 & CIITA) and their differentiallyexpressed targets (Table 2) were found to be involved in inflammation,priming of adaptive immune response, and glucocorticoid receptorsignaling (FIG. 7A and FIG. 7B). FIG. 7B shows transcription factorstargeting RT-PCR assayed and differentially regulated genes. Both MYCand NR3C1 were predicted to be activated (according to predictionz-score value, which were >2.5). The top function associated with thesetargets were apoptosis of leukocytes, hematopoisis, proliferation ofblood cells, immune response; and top pathways are given in the tableimmediately below in Table A:

TABLE A Network showing MYC and NR3C1 targets among immune responsegenes Symbol EntrezID FC Family Drugs Entrez Gene Name ACTB 60 −1.73other actin, beta AKT1 207 −3.13 kinase enzastaurin v-akt murine thymomaviral oncogene homolog 1 CASP1 834 −1.58 peptidase caspase 1,apoptosis-related cysteine peptidase CD44 960 −2.33 other CD44 molecule(Indian blood group) CDKN1A 1026 −2.92 kinase cyclin-dependent kinaseinhibitor 1A (p21, Cip1) HLA-A 3105 −2.63 other major histocompatibilitycomplex, class I, A ICAM1 3383 −2.09 transmembrane intercellularadhesion molecule 1 receptor IL8 3576 −1.53 cytokine interleukin 8 ITGAM3684 −2.02 other integrin, alpha M (complement component 3 receptor 3subunit) ITGB2 3689 −1.29 other integrin, beta 2 (complement component 3receptor 3 and 4 subunit) MYC 4609 transcription v-myc myelocytomatosisviral regulator oncogene homolog (avian) NFKB1 4790 −1.56 transcriptionnuclear factor of kappa light regulator polypeptide gene enhancer inB-cells 1 NFKB2 4791 −1.44 transcription nuclear factor of kappa lightregulator polypeptide gene enhancer in B-cells 2 (p49/p100) NR3C1 2908ligand-dependent rimexolone, nuclear receptor subfamily 3, group nuclearreceptor C, member 1 (glucocorticoid receptor) RELA 5970 −1.72transcription NF-kappaB v-rel reticuloendotheliosis viral regulatordecoy oncogene homolog A (avian) TLR2 7097 −3.14 transmembrane toll-likereceptor 2 receptor TNF 7124 −3.74 cytokine adalimumab tumor necrosisfactor TNFAIP3 7128 −3.74 enzyme tumor necrosis factor, alpha-inducedprotein 3 TNFRSF10B 8795 −1.71 transmembrane tigatuzumab tumor necrosisfactor receptor receptor superfamily, member 10b

Regulatory sites for a number of transcription factors including SP1,CREB1, ATF6, cEBP, and binding sites for the defense critical—NFkBtranscription factors complex, and stress response sites (STRE) wereamong common regulatory motifs identified for some of stress-suppressedgenes, STRE site being predicted to be regulated by MAZ and MZF1. Stressactivated factors included GFI1, MYC, FOXM1, GLI2, MAX and HNF1A (Table2), and these factors induced genes important for hormone biosynthesisand suppressed immune related genes.

FIG. 7A shows transcription factors predicted to be inhibited bybattlefield stressors and their targets among stress modulated genes.Shown here are transcription factors predicted to be inhibited bybattlefield stessors (Table 2) and their targets among 288stress-affected transcripts (filtered using Welch's t-test and FDR,q<0.001, and >1.5 fold change). Enriched function and pathways of thesetranscripts include activation and proliferation of leukocytes,maturation of dendritic cells (DCs), communication between innate andadaptive immunity, glucocorticoid receptor signaling and antigenpresentation pathway.

TABLE 2 Predicted transcription factors and targets identified among1396 genes that passed Welch's t-test, FDR correction (q ≦ 0.05) and 1.5fold change cutoff. z- p- TF score value target molecules in datasetactivated transcription factors and targets GFI1 3.1 4.1E−04 CASP1,CDKN1A, CEBPA, GUSB, ICAM1, IL1A, IL1B, IL8, IRF1, MMP7, NFKB1, NFKB2,RELA, RELB, TRAF3 MYC 3 1.6E−17 ACAT1, ACTB, ACTN1, AFP, AHCY, ALB,BCAT1, BCL6, BIN1, BIRC2, BIRC5, CAPN2, CASP1, CASP10, CAV1, CCND1,CCND3, CD44, CD48, CDC20, CDH2, CDK1, CDK11A/CDK11B, CDKN1A, CEBPA,COL14A1, COL1A1, CSPG4, CYFIP2, DDX11/ DDX12, DDX3X, DDX5, DUSP6, EDN1,EGR2, EIF2S2, F2, F3, FBN1 FOXM1 2.8 4.8E−05 BIRC5, CCND1, CDC20, CDK1,CDKN1A, CENPA, CENPF, FOXM1, KDR, KIF20A, MMP2, PLK4, TGFBR2 GLI2 2.73.2E−02 CCL5, CCND1, CDK1, CDKN1A, IL1B, ITGB1, KRT1, KRT17, PTCH1,SFRP1 MAX 2.4 1.4E−03 BCL6, CDKN1A, EDN1, FTH1, ID1, KLF6, LAMP2,MTHFD1, PDGFRB, SERINC3, TSC2, UBE2C HNF1A 2.1 3.6E−02 ABCC2, AFP,AKR1C4, ALB, ANPEP, APOB, AQP9, BCL6, C2, CCND1, DPP4, DUSP6, FAM107B,FBXO8, FGA, FGB, G0S2, GNB2L1, HNF4A, IGFBP1, KIF20A, KIR3DL1, LCAT,MTHFD1, NAPA, PDK1, PFKP, PIH1D1, PRLR, PZP, SERPINA7, SLC26A1, SLCO1A2,SSTR4, TRA@, UQCRC2, UROD inhibited transcription factors and targetsCEBPB −2.2 1.3E−11 ACTG2, ALB, C3, CCL5, CCND1, CD14, CDKN1A, CEBPA,CEBPB, COL1A1, CP, CSF1R, CTSC, CXCL5, CYP19A1, DDX5, DEGS1, FTL, HLA-C,HP, HSPD1, ICAM1, ID1, IGFBP1, IL1B, IL1RN, IL8, INMT, IRF9, LAMC1,LCP2, LYN, MGP, MIA, PCTP, PDGFRA, PEA15, PLAUR, PPARD, PRKCD, PR JUNB−2.3 2.8E−03 ACLY, CAV1, CCND1, CD68, CDC20, COL1A1, CYP19A1, FTH1,MMP2, MVD, NCF2, PTBP2, RELB, SCD CIITA −2.4 1.4E−07 B2M, CCND1, CD74,COL1A1, HLA-B, HLA-DOB, HLA-DPA1, HLA-DQA1, HLA-DQB1, HLA-DRA, HLA-DRB1POU2AF1 −2.6 3.4E−03 BCL6, CCND3, CD79A, CD79B, IGHA1, IGHG1, LCK, TRAF3STAT1 −2.8 8.2E−12 A2M, B2M, BIRC5, BTG1, C3, CASP1, CASP2, CASP4, CCL5,CCND1, CCND3, CCR7, CD14, CDKN1A, DPP4, FCER1G, GATA3, GBP1, GZMB,HLADRB1, ICAM1, IFIT3, IL1B, IL8, IRF1, IR5, IRF7, IRF9, LY96, NFE2,PDGFRB, PF4, PRL, PSMB10, PTGS2, SMAD7, SOCS3, STAT2, TLR4, TN FOXO3−2.8 1.8E−04 BIRC5, CCND1, CDKN1A, CTGF, CYR61, FOXM1, FOXO1, GPX1,IER3, IGFBP1, IL8, NAMPT, NOS3, SATB1, SOD2, TNFRSF1B, TXNIP, UBC, UBE2CSPI1 −2.9 1.5E−10 ACTB, CCR7, CD14, CD68, CD79A, CD79B, CEBPA, CSF1R,CYBB, DUSP6, FCER1G, FLI1, FTH1, GNB2L1, GPX1, IGL@, IL1B, IL1RN, IRF9,ITGA5, ITGAM, ITGB2, MCL1, MMP2, NCF2, P2RY1, PIK3CG, PTGS2, PTPRC,RELA, TK1, TLR2, TLR4 IFI16 −3 1.8E−04 CCL5, CCND1, CDKN1A, EDN1, GPX1,ICAM1, IFI16, IL1B, IL1RN, IL2RB, IL8, RPA3, STAT2 HMGB1 −3.1 1.6E−06CD83, CDKN1A, CXCL5, HLADRB1, ICAM1, IL1A, IL1B, IL8, MIA, PTGS2, RELB,SIRT1, TLR2, TLR4 IRF1 −3.2 1.0E−06 B2M, CASP1, CASP2, CCL5, CCND1,CDKN1A, CYBB, EIF4A3, HLA-G, IFIT3, IL1B, IL8, IRF1, IRF5, IRF7, IRF9,LTB, NFE2, PF4, PSMB10, PTGS2, SOCS7, STAT2, TRIM22 CREB1 −3.4 1.5E−08ARPC3, ATP6V0B, BTG2, CCND1, CD3D, CD4, CD68, CD79A, CDH2, CEBPB,CYP19A1, CYP51A1, CYR61, DIO2, EDN1, EGR2, FN1, FOSB, GALNT1, HERPUD1,HLA- DRA, HLA-G, HMGCS1, HSPA4, IL1B, INHA, IRF7, MCL1, PDE3B, PDGFRA,PER1, PRL, PTGS2, SCD, SLC16A1, SLC2A4, SOD2, TF, TFAP2A, UPP1 NFKB1−3.4 1.9E−08 A2M, ADORA1, AKR1B1, B2M, BTG2, CCL5, CCND1, CDKN1A,COL2A1, CYBB, FANCD2, GATA3, GNB2L1, ICAM1, IER3, IFNGR2, IGHG1, IL1B,IL1RN, IL8, IRF1, LTB, MICA, NFKB1, NFKB2, PLK3, POU2F2, PRKACA, PTGS2,RELA, RELB, SOD2, TK1, TLR2, TNFAIP3 RELA −3.7 3.1E−17 A2M, ABCG2,ACTA2, AFP, B2M, BIRC2, BTG2, CAV1, CCL5, CCND1, CCR7, CD44, CDKN1A,COL2A1, CXCL1, CYBB, CYP19A1, DIO2, EDN1, EWSR1, F3, GDF15, HLA-B,ICAM1, IER2, IER3, IFNGR2, IGHG1, IL1A, IL1B, IL1RN, IL8, INPP5D, IRF1,IRF7, L IRF7 −3.9 3.0E−03 CASP4, CCL5, GBP1, IFI16, IFIT3, IRF1, IRF9,ISG20, ITGAM, MCL1, NAMPT, PSMB10, STAT2, TLR4, TMPO, TRIM21, TRIM22Abbreviation: TF, transcription factor/regulator. Regulation z-score;P-value overlap.

SUMMARY

Most immune response genes were down-regulated in post-Trainingleukocytes compared to pre-Training leukocytes. Functional enrichment ofthese down-regulated genes revealed their involvement in microbialpattern recognition, cytokine production and reception, chemotaxis,intercellular adhesion, immunological synapse formation, regulation ofimmune response, and activation and proliferation of immune cells (FIG.8).

FIG. 8 demonstrates a functional network of differentially expressedgenes connected by their sub-functions in the immune system. The networkshows enriched functions of genes involved in immune responses:activation of immune cells, differentiation, proliferation, antigenpresentation, and infection directed migrations. Genes involved in allthese functions were down regulated by the Ranger Training stressors.Each node represents a category of gene ontology of the pathways of theimmune system. Node sizes are proportional to the number of genes belongto each category according to gene ontology, and intensity of nodeindicate significance of hypergeometric test after Bonferroni correction(q≦0.05). The pattern circles show more significant the enrichment thanthe solid white circles.

Our data suggest that stress induced suppression of microbial patternsof innate immunity (FIG. 9A) may impair infection-directed maturation,activation, inflammatory response, motility, and proliferation ofmyeloid cells (FIGS. 9B & 9C) These impaired innate cells may also failin priming the adaptive arm of immune response (FIG. 10A).

In FIG. 9A, shows altered immune response genes involved in patternrecognition, viral, antibacterial effector (humoral) responses.

In FIG. 9B, roles of stress down regulated genes in the cellularpathways of immune response are shown. Flat-ended arrows representsuppression of the corresponding pathway (biological process). Microbialrecognition receptors, inflammatory cytokines (IL1, IL1R, TNFα, CD40),chemotaxis (IL8, IL8R, RANTES, CCR5, CCR7), lymphocyte recruitment (IL4,IL 12), and production of effector molecules (INFγ, IL2, IL2RB) weredown regulated after Ranger Training

In FIG. 9C, actions of secreted cytokines on other leukocytes are shown.Impaired activity of suppressed IL-1 other myeloid cells to secretantimicrobial effector molecules; depleted concentration gradient ofIL-8 providing curtailed guidance to neutrophils and NK cells to sitesof infection, and suppressed IL-8 and RANTES unable to recruit andinduce maturation of dendritic cells (for antigen presentation);suppressed transcripts important for T-cell polarization (cellular orhumoral) may mean deprivation of the host under stress from havingprotective immunity.

FIGS. 10A and 10B show stress-suppressed genes involved in antigenpresentation and synapse formation. FIG. 10A shows antigen presentationpathways: This KEEG pathway taken via IPA was colored for the 288stress-regulated genes that passed Welch's t-test, FDR correction(q≦0.001) and changed by ≧1.5 fold (between pre- and post-Traininggroups).

FIG. 10B shows expression of genes important for immunological synapseformation; suppression of transcripts important in antigen preparation,presentation, chemotaxis, intercellular binding, antigen reception, anddownstream signaling (the gene labeled solid nodes) may have impairedformation of productive immunological synapse, and hence the poorresponse of post-Training leukocytes to SEB challenge although SEB toxinis presented without undergoing intracellular preparation, antigenpresenting molecules of the synapse were suppressed.

Adaptive cells' antigen receptors, co-receptors, signal transducers,intercellular adhesion molecules, and chemokine receptors were highlysuppressed (FIG. 10B). It is less likely that these stress-debilitatedlymphocytes can be activated, proliferated, differentiated, and clonallyexpanded to amount defense response against infections as confirmed byimpaired response of post-Training leukocytes to SEB exposure.

Discussion

Suppression of transcripts of critical immune response pathways, andregulatory networks are consistent with impaired innate and adaptiveimmune responses, including cellular and humoral immunity, as a resultof battlefield-like stress.

Down-regulation of transcripts involved in Toll-like receptor, andchemokine and chemokine receptor signaling pathways indicate suppressedinflammatory response, impaired maturation of antigen presenting cells(APCs), impaired affinity maturation of integrins, and impairedmigration, extravasation & homing of APCs and T-cells to nearby draininglymph nodes or infection sites.

Antigen preparation and presentation was the most suppressed pathwayamong immune response processes (FIG. 11). FIG. 11 shows canonicalpathways significantly associated with stress-regulated genes thatpassed Welch's t-test and FDR correction (p<=0.001) and 1.5 fold change.Numbers on the right side indicate total # of genes in the pathway.Suppression of antigen presentation, T-cell receptor and integrinpathways indicate lack of productive immunological synapse formation(poor MHC-restricted antigen recognition and T-cell activation), leadingto impaired adaptive and effector immune responses. Particularly,suppression of transcripts involved in cytoskeleton-dependent processes(chemokine guided migration, integrin-mediated adhesion,immunological-synapse formation, cellular polarization, andactin-microtubule aided receptor sequestration and signaling) curtailsthe dynamic cellular framework of T-cell activations (FIG. 10).

Unlike reports of differential regulations of Th1 and Th2 type responsesobserved in college students on the day of a stressful examination¹⁵,and in caregivers of chronically sick relatives¹⁶, our data suggest thatbattlefield-like stressors impair not only Th1 but also Th2 typeresponses as shown by suppressed transcripts of TLR2 and 4, and thecytokines IL4, IL4R and IL10RA in post-Training leukocytes. Suppressionof inflammatory molecules (e.g., IL1A & 1B, and IL1R1, TNF members andTNF receptors, and NFkB class of factors), and Th2 classes of cytokinesshow features of battlefield-like stress that are distinct from acuteand psychological stresses.

Previously miR-155 is reported to be proinflammatory. MiR-155 (−/−) miceare highly resistant to experimental autoimmune encephalomyelitis¹⁷, andshow suppressed antigen-specific helper cell, and markedly reducedarticular inflammation¹⁸. Here, miR-155 transcripts were elevated inpost-Training leukocytes (with or without SEB exposure), but itsexpression was suppressed by SEB in pre-training leukocytes (FIG. 6).

It seems that miR-155 is anti-inflammatory in humans exposed to stressand SEB toxin. Regulatory connection of miR-155 to many ofstress-suppressed inflammatory cytokines may indicate its involvement inregulation of these cytokines, and glucocorticoid receptor elements, andmodulate maturation of antigen presenting cells under battlefield-likestress.

Poor response of post-Training leukocytes to SEB ex vivo challenge isconsistent with suppressed expression of MHCs, T-cell receptors,co-receptors and integrins which are important for activations of APCsand T-cells. Overall, our results clearly demonstrated thatbattlefield-like stressors suppress a broad spectrum of immune systemprocess. This suppression of broad categories of immune responsepathways may explain why chronically stressed individuals show poorvaccine responses and susceptibility to infections.

FIGS. 12-15 were generated from nearest shrunken centroid prediction.The Nearest Shrunken Centroid (NSC) classifier (predictor) is arobust²¹⁻²² way of identifying genes specific to a certain agent in thepresence of other infections or conditions²³. NSC was used successfullyto identify cancer biomarkers²⁴⁻²⁵ and other disease sub-typing²⁶⁻²⁷.

FIG. 12 is a graphical representation of Nearest shrunken centroid (NSC)ranked genes when stressed and control groups compared. The length ofthe horizontal bars indicate the absolute value of the score (the biggerthe absolute value of the score the longer the horizontal bar, and thedirection indicate the gene expression direction (left oriented barindicate down-regulated and right oriented bar up-regulated genes). Hereonly two groups are compared and the opposite orientations of thehorizontal bars indicate that these genes discriminate between the twocompared groups.

FIGS. 13A and 13B are graphical representations of NSC algorithmidentified genes which can discriminate stress and other conditions(dengue virus exposure, Yersinia pestis or plague infection and SEBtoxin exposure; and also unexposed control group). The direction andlength of the horizontal bars is given in FIG. 12. As shown in FIGS. 13A&B there are 69 genes including 10 specific to the other pathogens thatare shown by the corresponding horizontal bars.

FIG. 14 shows misclassification error versus threshold (cut-off) values,each line representing each condition. Here the stress (black line) hasthe lowest misclassification error beyond the threshold value of around2.6. That means, genes ranked from one to about 260 can discriminatestress from other conditions (shown here). But in our case we took thetop ranked genes (even though many more can also be potential stressbiomarkers).

FIG. 15 is a graph showing that identified genes were cross-validated toascertain that they were not included by mere chance. The more opencircles (under stress) being separated from other shapes indicate thatthese genes discriminate stressed individuals from other patients(samples collected from patients exposed to other pathogens or controlgroup). Though there is shown in FIG. 15 only 114 samples, the totalnumber of samples used for prediction were 141.

Conclusion

Suppressed expression of genes critical to innate, humoral and cellularimmunity is an indicator of compromised protective immunity as confirmedby impaired response of post-Training leukocytes to SEB challenge.Numbers and ratios of different subpopulations of leukocytes beingwithin normal ranges, our observation (of anergic leukocytes of severelystressed individuals) draws some caution on current diagnostic practiceof counting immune cells to ascertain integrity of the immune system,and its ability of protection against infection.

On the basis of suppressed inflammatory molecules and pathways, wehypothesized that exposure to battlefield-like and similar stresses maymake exposed individuals less susceptible to autoimmune diseases, andsepsis; yet they may easily succumb to toxin or infection since theirprotective immunity already depleted.

Characterization of molecular signatures of stress pathologies canpotentially reveal biomarkers and new pharmacologic targets forimproving adaptation to stress and preventing stress-inducedpathogenesis. Results such as ours together with proteomic analyses mayyield novel preventative, prognostic and therapeutic opportunities tointervene the negative consequences of stress on heath.

Materials and Methods Blood Sample Collection

Whole blood (from each subject) was drawn in Leucopack tubes (BRTLaboratories Inc., Baltimore, Md.) before and after the eight-weekTraining, and immediately spun at 200×g for 10 minutes. The concentratedleukocyte layer (buffy coats) was collected and treated with TRIzol™reagent (Invitrogen, Carlsbad, Calif.) for RNA isolation and then storedat −80° C. Differential and complete blood counts (CBC) were obtainedimmediately after blood collection using a hemocytometer, andsubsequently using an ABX PENTRA C+ 60 flow cytometer (Horiba ABX,Irvine, Calif.). Blood samples were also collected in PAXgene™ Blood RNATubes (VWR Scientific, Buffalo Grove, Ill.) for direct RNA isolation.

RNA Isolation

For cDNA microarray analysis, total RNA was isolated using the TRIzol™reagent according to the manufacturer's instructions. The RNA sampleswere treated with DNase-1 (Invitrogen, Carlsbad, Calif.) to removegenomic DNA and were re-precipitated by isopropanol. The TRIzol™isolated RNA was used in cDNA microarrays analysis¹⁹. Foroligonucleotide microarrays, total RNA was isolated using PAXgene tubesfollowing the manufacturer's protocol. The PAXgene tube contains aproprietary reagent that immediately stabilizes RNA at room temperature(18-25° C.) without freezing. Isolated RNA samples were stored at ˜80°C. until they were used for microarray and real time PCR analyses. Theconcentration and integrity of RNA were determined using an Agilent 2000BioAnalyzer (Palo Alto, Calif.) according to manufacturer'sinstructions. The ArrayControl RNA Spikes from Ambion (Austin, Tex.)were used to monitor RNA integrity in hybridization, reversetranscription and RNA labeling.

cDNA Synthesis, Labeling, Hybridization and Image Processing

RNA was reverse transcribed and labeled using Micromax Tyramide SignalAmplification (TSA) Labeling and Detection Kit (Perkin Elmer, Inc.,Waltham, Mass.) following the manufacturer's protocol. The slides werehybridized at 60° C. for 16 h (for cDNA microarrays and Trizol isolatedRNA) and at 55° C. for 16 h (for oligonucleotide microarrays and PAXgenisolated RNA). Hybridized slides were scanned and recorded using aGenePix Pro 4000B (Axon Instruments Inc., Union City, Calif.) opticalscanner, and the data were documented using Gene Pix 6.0 (AxonInstruments Inc, Union City, Calif.).

Preparation of cDNA Microarrays

Human cDNA microarrays were prepared using sequence-verified PCRelements produced from ˜10,000 well-characterized human genes of TheEasy to Spot Human UniGEM V2.0 cDNA Library (Incyte Genomics Inc.,Wilmington, Del.). The PCR products, ranging from 500 to 700 base pairs,were deposited in 3× saline sodium citrate (SSC) at an averageconcentration of 165 μg/ml on CMT-GAPS™ II (γ-aminopropylsilane) coatedslides (Corning Inc., Corning, N.Y.), using a Bio-Rad VersArrayMicroArrayer (Hercules, Calif.). The cDNAs were UV-cross-linked at 120mJ/cm² using UV Stratalinker® 2400 from Stratagene (La Jolla, Calif.).The microarrays were baked at 80° C. for 4 h. The slides were treatedwith succinic anhydride and N-methyl-2-pyrrolidinone to remove excessamines.

Oligonucleotide Microarrays

The Human Genome Array Ready Oligo Set Version 3.0 Set from OperonBiotechnologies (Huntsville, Ala.) includes 34,580 oligonucleotideprobes representing 24,650 genes and 37,123 RNA transcripts from thehuman genome. The oligonucleotide targets were deposited in 3× salinesodium citrate (SSC) at an average concentration of 165 μg/ml ontoCMT-GAPS II aminopropylsilane-coated slides (Corning, Corning, N.Y.)using a VersArray Microarrayer. Microarrays were UV-crosslinked at 120mJ/cm² using UV Stratalinker® 2400. Then slides were baked at 80° C. for4 hours, and were treated with succinic anhydride andN-methyl-2-pyrrolidinone to remove excess amines on the slide surface.Slides were stored in boxes with slide racks and the boxes were kept indesiccators.

Real Time QPCR

Quantitative real time PCR arrays of one hundred genes associated withinflammation, transcription factors, and antigen preparation andpresentation pathways were carried out using Dendritic & AntigenPresenting Cell Pathway (PAHS 406) and NFkB Pathway (PAHS 25) RT²Profiler™ PCR Arrays (SABiosciences, Frederick, Md.) according tomanufacturer's instructions. Four replicates of RNA samples isolatedusing PAXgene™ from Trainees before and after Training were assayed. Thedata were analyzed using ABiosciences' web-based software.

Reverse transcriptase reagent (iScript) and real time PCR master mix(QuantiTect™ SYBR. Green PCR Kit) were obtained from BioRad Inc., CA andQIAGEN Inc., Valencia, Calif., respectively. Real time polymerase chainreactions (PCR) were carried out in i-Cycler Real-time PCR apparatus(BioRad Inc, Milpitas, Calif.), using three to five biologicalreplicates for each primer pair (based on sample availability). Thecustom oligonucleotide primers were designed using Primer3 software(www.basic.nwu.edu/biotools/Primer3.html), or based on those from UniSTS(http://www.ncbi.nlm.nih.gov/genome/UniSTS) and Universal Probe Libraryfor Human (Roche Applied Science). Their specificities were verified inthe BLAST domain at NCBI. Parallel amplification reaction using 18S rRNAprimers was carried out as a control. Threshold cycle (Ct) for every runwas recorded and then converted to fold change using the equation:[(1+E)^(ΔCt)]_(GOI)/[(1+E)^(ΔCt)]_(HKG), where ΔCt stands for thedifference between Ct of control and treated samples of a given gene,which is either gene of interest (GOI) or housekeeping genes (HKG), andE stands for primer efficiency, calculated from slope of best fittingstandard curve of each primer pair.

ELISA

Plasma concentrations of prolactin (PRL), insulin-like growth factors Iand II (IGF-I & II), tumor necrosis factor alpha (TNFα), and enzymaticactivity of superoxide dismutase were determined using ELISA kits fromCalbiotech, Inc. (Spring Valley, Calif., Catalog #PR063F), DiagnosticSystems Laboratories, Inc. (Webster, Tex., Catalog #s DSL-10-2800 andDSL-10-2600), Quantikine® of R&D Systems, Inc. (Minneapolis, Minn.,Catalog #DTA00C) and Dojindo Molecular Technologies, Inc (Gaithersburg,Md., Catalog #S311), respectively, following manufacturers' protocols.

Microarray Data Analyses

Background and foreground pixels of the fluorescence intensity of eachspot on the microarrays were segmented using ImaGene (BioDiscovery Inc.,El Segundo, Calif.) and the spots with the highest 20% of the backgroundand the lowest 20% of the signal were discarded. Local backgroundcorrection was applied. Genes that passed this filter in all experimentswere selected for further study. Then, sub-grid based Lowesnormalization was performed for each chip independently. Additional perspot (dividing by control channel) and per gene (to specific samples)normalization were also performed under the Genespring GX platform(Agilent Technologies Inc, Santa Clara, Calif.). Statistical analysiswas computed using Welch's t-test (p<0.05) with Benjamini and HochbergFalse Discovery Rate (FDR) Multiple Correction to select the genes withhigh altered expression (for cDNA microarray data, but oligonucleotidemicroarray data were analyzed without FDR Correction). Two-dimensionalclustering was carried out based on samples and genes for visualizationand assessment of reproducibility in the profile of the significantgenes across biological replicates.

Interaction Networks and Gene Ontology Enrichment

Bingo 2.3 was used for gene ontology enrichment with hypergeometricdistribution with FDR (false discover rate) or Bonferroni corrections(p<0.05). Biological processes, molecular functions, and cellularcomponents of each cluster of genes were compared to the globalannotations and over-represented categories after corrections wereanalyzed and visualized. Functional analysis and pathways associatedwith stress and pathogen-regulated genes were analyzed using IngenuityPathway Analysis (Ingenuity Systems Inc.; Redwood City, Calif.).Cytoscape Version 2.6.1 (http://www.cytoscape.org) was used forvisualizing and analyzing enriched gene ontologies, and molecularinteraction network constructions.

MicroRNA Analysis

Expression profiles of MicroRNAs were assayed using Agilent's humanmiRNA v3 microarray (Agilent Technologies Inc) consisting of 15 ktargets representing 961 microRNAs. Differentially expressed microRNAswere analyzed using Qlucore Omices Explorer 2.2 (Qlucore AB) andGeneSpring GX 11.5 (Agilent Technologies Inc.). Target transcripts ofprofiled microRNAs were identified using target scan of Genespring, andIngenuity Pathway Analysis (IPA) (Ingenuity Systems Inc.). Interactionnetworks of differentially expressed microRNAs and their target mRNAswere constructed using IPA.

Treatment of Leukocytes with Staphylococcal Enterotoxin B (SEB)

Leukocytes isolated from leucopack blood samples were plated in six welltissue culture plates (˜10⁶ cells/ml in RPMI 1640 and 10% human ABserum) and treated with SEB (Toxin Technology Inc., Sarasota, Fla.) at afinal concentration of 100 ng/ml SEB. Cells were incubated for 6 h at37° C. and 5% CO₂. At the end of the incubation period, treatedleukocytes were collected by centrifugation at 350×g for 15 minutes.Cell pellets were treated with 2 ml TRIzol™ and kept at −80° C. for RNAisolation.

cDNA Microarray (Expression) Data Based Prediction of TranscriptionFactors, Regulatory Binding Sites and Downstream Target Identification

Potential regulatory sites of differentially regulated genes wereidentified using HumanGenome9999 (Agilent Technologies Inc., CA)containing partial human genome sequences (9999 bp upstream region for21787 genes). Statistically significant (p<0.05) common regulatorymotifs of 5 to 12 nucleotides long were identified. The searching regionwas set to range 1 to 500 nucleotides upstream of transcription startsites. Other tools used for this purpose include MATCH and TFSEARCH.Cognate transcription factors of identified (common regulatory) siteswere searched from different prediction and repository databases: DBD(www.transcriptionfactor.org), JASPAR (http://jaspar.cgb.ki.se),TRANSFAC® 7.0—Public (http://www.gene-regulation.com/pub/databases.html)using ChipMAPPER²⁰, ConTra, Pscan and Ingenuity Pathway Analysis (IPA,ingenuity inc). Expression databased prediction Z-scores and regulatorytargets were analyzed using IPA. Regulator-target interaction networksand pathways were generated using Cytoscape (Cytoscape.org) and IPA.

TABLE 3A Transcripts that have passed Welch's T-TEST (& Bonferronicorrection at q < 0.01), and selected from battlefield-like conditionthat have Normalized Data values greater or less than those in baselinecondition by a factor of 3 fold (59 transcripts) Fold ID q-value changeSymbol UniGene Description AU119825 0.000726 3.29 A2M Hs.212838Alpha-2-macroglobulin BE889785 0.00932 −3.28 ACSL1 Hs.406678 Acyl-CoAsynthetase long-chain family member 1 AL558086 0.000818 9.06 ALBHs.418167 Albumin NM_001150 1.86E−05 −5.52 ANPEP Hs.1239 Alanyl(membrane) aminopeptidase (aminopeptidase N, aminopeptidase M,microsomal aminopeptidase, CD13, p150) [up-regulated in late adenovirustype-12 infection (Journal of Virology 2005, 79: 4, 2404)] BG5411300.000667 −3.52 ANXA1 Hs.494173 Annexin A1 NM_020980 5.62E−05 −8.06 AQP9Hs.104624 Aquaporin 9 [Dehydration/osmotic adaptation in yeast (JBC2005; 280: 8, 7186); specialized leukocyte functions such asimmunological response and bactericidal activity (PUBMED)] BF4320720.00212 −3.68 ATP2B1 Hs.506276 ATPase, Ca++ transporting, plasmamembrane 1 AV710740 4.47E−07 −3.91 B2M Hs.534255 Beta-2-microglobulinNM_012342 0.00103 3.36 BAMBI Hs.533336 BMP and activin membrane-boundinhibitor homolog (Xenopus laevis) AI348005 0.00671 −3.42 BTG1LHs.710041 Similar to B-cell translocation gene 1, XM_008651 4.30E−07−16.98 CCR7 chemokine (C-C motif) receptor 7 [suppression lead toimpaired lymphocyte migration, delayed adaptive immune response (cell1999), CCR7 is key mediator in balancing immunity and tolerance,abnormalities contribute to immune dysregulation (clinical andexperimental immunology, 2009)] AL549182 0.00137 −3.46 CD14 Hs.163867CD14 molecule M24915 0.000223 −4.9 CD44 Hs.502328 CD44 molecule (Indianblood group) BG333618 0.00854 −12.3 CD74 Hs.436568 CD74 molecule, majorhistocompatibility complex, class II invariant chain L26165 0.00869 −3.8CDKN1A Hs.370771 Cyclin-dependent kinase inhibitor 1A (p21, Cip1)NM_005196 0.00289 3.08 CENPF synonyms: CENF, PRO1779; centromere proteinF (400 kD); centromere protein F (350/400 kD, mitosin); CENP-Fkinetochore protein; AH antigen; cell-cycle-dependent 350K nuclearprotein; Homo sapiens centromere protein F, 350/400ka (mitosin) (CENPF),mRNA. AL570594 5.07E−05 4.15 COL6A1 Hs.474053 Collagen, type VI, alpha 1BE252062 0.000478 −3.92 CORO1A Hs.474053 Coronin, actin binding protein,1A NM_005211 6.28E−06 −3.25 CSF1R Hs.586219 Colony stimulating factor 1receptor, formerly McDonough feline sarcoma viral (v-fms) oncogenehomolog AU118073 0.00469 −4.52 CSPG2/ Hs.643801 Chondroitin sulfateproteoglycan 2 VCAN (versican) BG491425 0.000933 −15.22 CXCL1 Hs.789Chemokine (C-X-C motif) ligand 1 (melanoma growth stimulating activity,alpha) [involved in neurophil recruitment (Shock 35: 6, 604)] NM_0053660.000153 −3.34 MAGEA11 Hs.670252 Melanoma antigen family A, 11 AL5835930.0035 −7.3 FCN1 Hs.440898 Ficolin (collagen/fibrinogen domaincontaining) 1 [expressed at the cell surface of monocytes andgranulocytes and its receptor is found at activated but not resting Tlympohcytes (journal of leukocyte biology 2010; 88; 1: 145); it is partof the innate immune system and function as recognition molecules in thecomplement system (Journal of innate immunity 2010; 2: 1, 3)] NM_0134090.002 3.01 FST Hs.9914 Follistatin Z97989 0.00897 −3.82 FYN FYN oncogenerelated to SRC, FGR, YES NM_001472 9.99E−06 3.82 GAGE7 Hs.460641 Gantigen 7 AL551154 0.000131 −6.99 HCLS1 Hs.14601 Hematopoieticcell-specific Lyn substrate 1 [induces G-CSF-Triggered GranulopoiesisVia LEF-1 Transcription Factor (blood 2010 114: 22, 229); mutationdefects at HCLS1 with Kostmann disease, recombinant human granulocytecolony-stimulating factor (G-CSF), the prognosis and quality of lifeimproved dramatically (European Journal of Pediatrics 2010, 169: 6,659)] BG327758 0.00021 −15.13 HLA-B Hs.77961 Major histocompatibilitycomplex, class I, B BE168491 0.00123 −7.63 HLA-C Hs.654404 Majorhistocompatibility complex, class I, C AW407113 2.66E−05 −5.29 IGKV@,Hs.660766 Immunoglobulin kappa variable group AV759427 0.000205 −6.8HLA-DPA1 Hs.347270 Major histocompatibility complex, class II, DP alpha1 BF795929 0.00253 −8.33 HLA-DRA Hs.520048 Major histocompatibilitycomplex, class II, DR alpha M20503 0.000575 −11.82 HLA-DRB1/ Hs.696211/Major histocompatibility complex, class HLA-DRB5 II, DR beta 1/5BF974114 0.00046 −5.24 HLA-DRB1 Hs.696211 Major histocompatibilitycomplex, class II, DR beta 1 BF732822 0.000358 −4.98 HLA-DRB1 Hs.696211Major histocompatibility complex, class II, DR beta 1 AW411300 0.002674.36 IGF2 Hs.272259 Insulin-like growth factor 2 (somatomedin A)AL542262 0.00121 5.48 IGFBP1 Hs.642938 Insulin-like growth factorbinding protein 1 AI634950 9.18E−07 −11.82 IGHG1 Hs.510635Immunoglobulin heavy constant gamma 1 (G1m marker) AA490743 0.001 −4.61IGHG1 Hs.510635 Immunoglobulin heavy constant gamma 1 (G1m marker)NM_000575 0.00594 −5.15 IL1A Hs.1722 Interleukin 1, alpha W383196.35E−06 −6.29 IL1B Hs.126256 Interleukin 1, beta AU122160 0.000811−4.17 LAIR1 Hs.572535 Leukocyte-associated immunoglobulin- like receptor1 NM_006762 5.04E−07 −16.13 LAPTM5 Hs.371021 Lysosomal associatedmultispanning membrane protein 5 [negative regulation of cell surfaceBCR levels and B cell activation (The Journal of Immunology, 2010, 185:294-301); LAPTM5 negatively regulated surface TCR expression byspecifically interacting with the invariant signal-transducing CD3 zetachain and promoting its degradation without affecting other CD3proteins, CD3 epsilon, CD3 delta, or CD3 gamma (IMMUNITY 29: 1 Pages:33-43)] BF035921 0.000407 −4.65 LCP1 Hs.381099 Lymphocyte cytosolicprotein 1 (L- plastin) NM_024318 0.000838 −3.65 LILRA6 Hs.688335Leukocyte immunoglobulin-like receptor, subfamily A (with TM domain),member 6 AL560682 0.00115 −8.2 IG heavy Hs.703938 Immunoglobulin HeavyChain Variable chain/ region LOC652128 NM_004811 0.0021 −4.12 LPXNHs.125474 Leupaxin BF792356 1.21E−05 4.04 MAGEA6 Hs.441113 Melanomaantigen family A, 6 AW966037 0.000159 3.1 MDK Hs.82045 Midkine (neuritegrowth-promoting factor 2) BE742106 9.14E−06 −4.03 MGAT1 Hs.519818Mannosyl (alpha-1,3-)-glycoprotein beta-1,2-N-acetylglucosaminyltransferase NM_002473 0.00506 −3.39 MYH9 Hs.474751Myosin, heavy chain 9, non-muscle AU142621 0.00726 −4.46 PNP Hs.75514Nucleoside phosphorylase XM_007374 0.00795 −3.25 PRKCH protein kinase C,eta BE266904 7.79E−05 −4.15 SATB1 Hs.517717 Special AT-rich sequencebinding protein 1 (binds to nuclear matrix/scaffold-associating DNA's)AL550163 0.00157 −28.25 SERPINB2 Hs.594481 Serpin peptidase inhibitor,clade B (ovalbumin), member 2 [upregulated under different inflammatoryconditions, null mice showed increased TH1 response, secreted bymacrophages, hemotpoeitic and nonhematopoeitic cells] BG035651 0.00108−10.34 SOD2 Hs.487046 Superoxide dismutase 2, mitochondrial [Conditionalloss of SOD2 led to increased superoxide, apoptosis, and developmentaldefects in the T cell population, resulting in immunodeficiency andsusceptibility to the influenza A virus H1N1 (Free radical biology andmedicine, 201; 50: 3, 448); manipuation of SOD2 affects drosophilasurvival under stress (PLoS One 2011; 6: 5, e19866)] AL548113 4.31E−05−3.28 ST14 Hs.504315 Suppression of tumorigenicity 14 (colon carcinoma)D86980 3.55E−07 −3.57 TTC9 Hs.79170 Tetratricopeptide repeat domain 9NM_003387 2.52E−05 −4.05 WIPF1 Hs.128067 WAS/WASL interacting proteinfamily, member 1

TABLE 3B Top 59 of stress specific genes ranked in order: Gene GeneDengue Yersinia Rank Accession Name Control Virus SEB Stress PestisDescription 1 XM_008651 CCR7 0.0943 0 0 −0.2854 0 chemokine (C-C motif)receptor 7 2 AI634950 IGHG1 0.1285 0 0 −0.2723 0 Immunoglobulin heavyconstant gamma 1 (G1m marker) 3 AU118073 CSPG2 0 0 0 −0.2638 0.0673Chondroitin sulfate proteoglycan 2 4 NM_006762 LAPTM5 0.1751 0 0 −0.25920 Lysosomal associated multispanning membrane protein 5 5 NM_005211CSF1R 0 0 0 −0.2147 0 Colony stimulating factor 1 receptor, 6 AL558086ALB −0.0559 0 0 0.2136 0 Albumin 7 AW407113 HLA-C 0 0 0 −0.2119 0 Majorhistocompatibility complex, class I, C 8 BF795929 HLA-DRA 0 0 0 −0.193 0Major histocompatibility complex, class II, DR alpha 9 AV759427 HLA-DPA10 0 0 −0.1885 0 Major histocompatibility complex, class II, DP alpha 110 AL549182 CD14 0 0 0 −0.187 0.0541 CD14 molecule 11 AL560682 LOC6521280 0 0 −0.183 0 Similar to Ig heavy chain V-II region ARH-77 precursor 12BE742106 MGAT1 0 0 0 −0.1764 0 Mannosyl (alpha- 1,3-)-glycoproteinbeta-1,2-N- acetylglucosaminyl transferase 13 AL551154 HCLS1 0.0306 0 0−0.1738 0 Hematopoietic cell-specific Lyn substrate 1 14 NM_001150 ANPEP0.0331 0 0 −0.1713 0 Alanyl (membrane) aminopeptidase (aminopeptidase N,aminopeptidase M, microsomal aminopeptidase, CD13, p150) 15 W38319 IL1B0.0424 0 0 −0.1624 0 Interleukin 1, beta 16 BG327758 IL1B 0.0702 0 0−0.1618 0 Major histocompatibility complex, class I, B 17 BE266904 SATB10 0 0 −0.1566 0 Special AT-rich sequence binding protein 1 (binds tonuclear matrix/scaffold- associating DNA's) 18 BF035921 LCP1 0 0 0−0.1546 0 Lymphocyte cytosolic protein 1 (L-plastin) 19 NM_020980 AQP90.0815 0 0 −0.1491 0 Aquaporin 9 20 M20503 HLA-DRB1 0.0071 0 0 −0.147 0Major histocompatibility complex, class II, DR beta 1 21 AU142621 NP 0 00 −0.1463 0 Nucleoside phosphorylase 22 AA334424 AFP 0 0 0 0.1439 0Alpha-fetoprotein 23 NM_001946 DUSP6 0 0 0 −0.1433 0.0044 Dualspecificity phosphatase 6 24 AV710740 B2M 0.0427 0 0 −0.1403 0 Beta-2-microglobulin 25 XM_003507 SCYB5 0 0 0 −0.1371 0 small induciblecytokine subfamily B (Cys- X-Cys), 26 AL583593 FCN1 0.0203 0 0 −0.1359 0Ficolin (collagen/fibrinogen domain containing) 1 27 BE878314 FTH1 0 0 0−0.1346 0 Ferritin, heavy polypeptide 1 28 BF732822 HLA-DRB1 0 0 0−0.1318 0 Major histocompatibility complex, class II, DR beta 1 29XM_003506 PPBP 0 0 0 −0.1312 0 pro-platelet basic protein (includesplatelet basic 30 J04162 FCGR3A 0 0 0 −0.1308 0.0905 Fc fragment of IgG,low affinity IIIa, receptor (CD16a) 31 AA490743 IGHG1 0 0 0 −0.1254 0Immunoglobulin heavy constant gamma 1 (G1m marker) 32 AL542262 IGFBP1 00 0 0.1214 0 Insulin-like growth factor binding protein 1 33 NM_003387WIPF1 0 0 0 −0.1193 0 WAS/WASL interacting protein family, member 1 34BF792356 MAGEA6 −0.0079 0 0 0.1181 0 Melanoma antigen family A, 6 35NM_004811 LPXN 0 0 0 −0.1162 0 Leupaxin 36 BG491425 CXCL1 0 0 0 −0.11380 Chemokine (C—X—C motif) ligand 1 37 NM_001472 GAGE2 −0.0189 0 0 0.11270 G antigen 2 38 L26165 CDKN1A 0 0 0 −0.1121 0 Cyclin-dependent kinaseinhibitor 1A (p21, Cip1) 39 NM_000569 FCGR3A 0 0 0 −0.1107 0 Fc fragmentof IgG, low affinity IIIa, receptor (CD16a) 40 D86980 TTC9 0.0306 0 0−0.0992 0 Tetratricopeptide repeat domain 9 41 Z97989 FYN 0 0 0 −0.09890 FYN oncogene related to SRC, FGR, YES 42 AL550163 SERPINB2 0.1069 0 0−0.0971 0 Serpin peptidase inhibitor, clade B (ovalbumin), member 2 43NM_005196 CENPF 0 0 0 0.095 0 Homo sapiens centromere protein F,350/400ka (mitosin) (CENPF), mRNA. 44 NM_004987 LIMS1 0 0 0 −0.0887 0LIM and senescent cell antigen-like domains 1 45 AW966037 MDK 0 0 00.0877 0 Midkine (neurite growth-promoting factor 2) 46 AX025098AX025098 0 0 0 −0.0871 0 unnamed protein product; Sequence 22 fromPatent WO0031532. 47 AU119825 A2M 0 0 0 0.0867 0 Alpha-2- macroglobulin48 BG333618 CD74 0 0 0 −0.0847 0 CD74 molecule, major histocompatibilitycomplex, class II invariant chain 49 N32077 IER3 0 0 0 −0.082 0Immediate early response 3 50 BE168491 HLA-B 0.0089 0 0 −0.0816 0 Majorhistocompatibility complex, class I, B 51 BG481840 ACTB 0 0 0 −0.0773 0Actin, beta 52 BG541130 ANXA1 0 0 0 −0.074 0 Annexin A1 53 AU122160LAIR1 0.0158 0 0 −0.0709 0 Leukocyte- associated immunoglobulin- likereceptor 1 54 M24915 CD44 0.0216 0 0 −0.0704 0 CD44 molecule 55 AL570594COL6A1 0 0 0 0.0678 0 Collagen, type VI, alpha 1 56 XM_007374 PRKCH 0 00 −0.0676 0 protein kinase C, eta 57 AA583143 MAFB 0 0 0 −0.0638 0 V-mafmusculoaponeurotic fibrosarcoma oncogene homolog B 58 XM_008466 EVI2A 00 0 −0.063 0 ecotropic viral integration site 2A 59 AA309971 LAT 0 0 0−0.0619 0 Linker for activation of T cells

TABLE 4 After 8 weeks: Transcripts profiled using quantitative real timeQPCR arrays (116 transcripts were down- regulated, and 3 transcriptswere up-regulated) Symbol Fold StdevRTPCR IKBKG −12.6188 0.339657363RELB −12.2737 0.284777655 IRAK1 −9.2375 0.360943649 HGDC −6.86850.390462704 JUN −5.9484 0.27944997 TNFSF14 −4.7158 0.433281621 RELA−3.9724 0.63019443 CD40 −3.7974 0.189280078 FADD −3.6364 0.367498363PPM1A −3.5988 0.27174874 INHBA −3.5247 0.104573154 CSF1R −3.18210.758689626 CXCL10 −3.1766 0.277406814 AKT1 −3.1059 0.367849974 TNFRSF1A−2.9079 0.687128349 ACTB −2.8481 0.351350801 TRADD −2.8432 0.506924503TLR9 −2.8382 0.289278965 TNFRSF10B −2.8284 0.267218757 LTBR −2.58470.570855834 CXCL1 −2.5403 0.579529817 FCER2 −2.5184 0.414730623 SLC44A2−2.4967 −0.822611762 HMOX1 −2.4368 0.155559063 CCL4 −2.4116 0.533964145CD209 −2.4074 0.197647764 IKBKE −2.3784 0.555233712 ICAM1 −2.33350.437161543 HLA-A −2.3295 1.214034504 ELK1 −2.3254 0.269089688 CCL3L1−2.2462 0.27090657 TNFAIP3 −2.2346 0.389174835 TLR6 −2.2191 0.872877926HLA-DOA −2.2153 0.607988424 MAP3K1 −2.2115 0.61339209 IKBKB −2.19620.538167096 NFKBIA −2.1772 0.152911234 F2R −2.1473 0.243094984 CDKN1A−2.1287 0.707160113 CFB −2.1287 0.164433367 CD28 −2.114 0.214883087 IL16−2.0958 −6.38481053 ERBB2 −2.0777 0.192737356 IRAK2 −2.0669 0.234239489CD1D −2.035 0.200278319 TLR2 −2.0279 −2.201882954 CCL8 −2.01390.148872434 CD4 −2 0.616064291 HLA-DMA −1.9793 1.430015754 FASLG −1.97250.132549302 CCL11 −1.9252 0.137200432 CCL13 −1.9252 0.137200432 CCL16−1.9252 0.137200432 CCL7 −1.9252 0.137200432 CXCL12 −1.9252 0.137200432CXCL2 −1.9252 0.137200432 FCAR −1.9252 0.137200432 IL2 −1.92520.137200432 MDK −1.9252 0.137200432 TNFSF11 −1.9252 0.137200432 IL12B−1.8823 0.139095667 CD40 −1.8693 0.396668136 HLA-DPA1 −1.8693−92.22884305 RELB −1.8661 0.207774407 REL −1.8628 0.585844588 TLR1−1.8628 0.602086965 CD2 −1.8468 0.857944585 ICAM1 −1.8182 0.63392797TAPBP −1.8119 0.419814619 RELA −1.7932 0.273669806 CASP8 −1.77770.21122336 IL1R1 −1.7685 0.524613114 TICAM2 −1.7623 0.216623278 CD1B−1.7381 0.132080179 CEBPA −1.7112 0.784622441 CASP1 −1.7082 0.934618998STAT1 −1.7082 0.964130752 TLR4 −1.7082 0.580800815 RAF1 −1.70231.180672752 CCR2 −1.6935 0.305351506 IFIT3 −1.6615 0.677571172 TNFRSF10A−1.6615 0.230520538 IFNGR1 −1.6558 1.492757528 ITGB2 −1.6558 21.10639135LYN −1.6558 230.7481187 CCL19 −1.6358 0.131657942 CCL5 −1.62171.745561311 RAC1 −1.5938 0.515059945 MALT1 −1.5883 0.281116286 CCL3−1.5692 0.165855825 CD80 −1.5665 0.132742476 TAP2 −1.5502 0.393041048ACTB −1.5369 0.382445363 IL8 −1.5157 0.483025481 CCL2 −1.51050.134605272 TLR3 −1.5 0.165275956 IL12A −1.4974 0.198792804 FCGR1A−1.4923 0.878361699 NFKB2 −1.4923 0.403365512 EDARADD −1.47940.143070569 NOD1 −1.4768 0.308099861 TRAP1 −1.4439 0.483257919 NLRP12−1.4439 0.363870333 PDIA3 −1.434 0.406179958 IL8 −1.4216 0.354085621HLA-DQA1 −1.4167 1.178062108 MIF −1.402 1.497615941 RPL13A −1.38991.4788335 ITGAM −1.3779 0.600004582 ATF1 −1.3519 0.183064879 CDC42−1.3496 3.310458234 ICAM2 −1.3426 0.973584543 CCR5 −1.3333 0.145884175CD44 −1.3036 1.754787134 IL8RA −1.3013 1.145515093 RIPK1 −1.30130.462210307 CCR3 1.402 0.384641887 TLR8 1.7471 2.50546893 TLR7 1.76540.64730932

TABLE 5 Average fold change: Stress-Regulated Genes Involved in ImmuneSystem Processes, oxidative stress response and steroid biosythesis.Functions were enriched using hypergeometric statistical analysis alongwith Bonferroni correction (p < 0.05). The significance level and foldchange for each gene (obtained from microarray statistical analysis) areshown in the last two columns respectively. Gene ID Name Descriptionfold p-value T-cell activation AW950965 CD3E CD3e, epsilon (CD3- −1.59.80E−03 TCR complex) BG333618 CD74 CD74, MHC, class II −12.3 2.90E−05invariant chain AA309971 LAT Linker for activation −2.9 3.10E−04 of Tcells NM_000887 ITGAX Integrin, alpha X −1.4 2.10E−02 (complementcomponent 3 receptor 4 subunit) NM_001767 CD2 CD2 molecule −1.3 3.40E−02AA766638 PAG1 Phosphoprotein −1.5 3.10E−02 associated withglycosphingolipid microdomains 1 XM_001772 LCK lymphocyte-specific −21.50E−04 protein tyrosine kinase NM_000616 CD4 CD4 molecule −2.31.00E−03 NM_000589 IL4 Interleukin 4 −1.6 6.60E−02 NM_002838 PTPRCProtein tyrosine −3.2 2.50E−03 BG391140 CSK C-src tyrosine kinase −1.55.00E−03 XM_006041 CD5 CD5 antigen (p56-62) −2.6 3.10E−04 M12824 CD8ACD8a molecule −3.9 1.20E−04 BC001257 GLMN Glomulin, FKBP −1.5 1.80E−02associated protein AA310902 CD3D CD3d molecule, −2.1 2.90E−03 delta(CD3-TCR complex) AI803460 CCND3 Cyclin D3 −1.5 8.80E−03 AC002310 ITGALintegrin, alpha L −1.4 7.30E−02 (antigen CD11A (P180), lymphocytefunction-associated antigen1; alpha polypeptide) NM_003177 SYK Spleentyrosine −1.8 7.60E−03 kinase NM_000632 ITGAM Integrin, alpha M −2.36.10E−04 (complement component 3 receptor 3 subunit) U81504 AP3B1Adaptor-related −1.6 1.00E−02 protein complex 3, beta 1 subunit AW780437PRKCQ Protein kinase C, −1.7 9.10E−03 theta AL136450 BCORL1 BCL6co-repressor- −1.7 3.90E−04 like 1 NM_004931 CD8B CD8b molecule −1.52.50E−03 B cell activation XM_003106 PRKCD protein kinase C, −1.98.80E−04 delta AU118181 KLF6 Kruppel-like factor 6 −2.6 3.70E−04NM_000589 IL4 Interleukin 4 −1.6 6.60E−02 NM_001250 CD40 CD40 molecule,−1.4 1.80E−02 TNF receptor superfamily member L26165 CDKN1ACyclin-dependent −3.8 2.90E−05 kinase inhibitor 1A (p21, Cip1) NM_003177SYK Spleen tyrosine −1.8 7.60E−03 kinase NM_002838 PTPRC Proteintyrosine −3.2 2.50E−03 phosphatase, receptor type, C Natural killer cellactivation NM_001767 CD2 CD2 molecule −1.3 3.40E−02 AI948861 SLAMF7 SLAMfamily −1.7 2.50E−02 member 7 AF285436 KIR3DL1 Killer cell −1.8 3.90E−04immunoglobulin-like receptor, three domains, long cytoplasmic tail, 1AL136450 BCORL1 BCL6 co-repressor- −1.7 3.90E−04 like 1 Myeloiddendritic cell activation NM_001767 CD2 CD2 molecule −1.3 3.40E−02NM_006509 RELB V-rel −1.9 1.30E−04 reticuloendotheliosis viral oncogenehomolog B, nuclear factor of kappa light polypeptide gene enhancer inB-cells 3 (avian) Mast cell activation AA309971 LAT Linker foractivation −2.9 3.10E−04 of T cells AF177765 TLR4 toll-like receptor 4−1.8 9.60E−03 (TLR4) NM_005565 LCP2 Lymphocyte −2.5 9.30E−04 cytosolicprotein 2 (SH2 domain containing leukocyte protein of 76 kDa) NM_003177SYK Spleen tyrosine −1.8 7.60E−03 kinase Macrophage activation BG333618CD74 CD74; MHC, class −12.3 2.90E−05 II invariant chain AI937452 CD93CD93 molecule −1.6 5.60E−04 AF177765 TLR4 toll-like receptor 4 −1.89.60E−03 (TLR4) Platelete activation AI739539 PF4 Platelet factor 4 −3.31.10E−04 (chemokine (C-X-C motif) ligand 4) NM_001250 CD40 CD40molecule, −1.4 1.80E−02 TNF receptor superfamily member T-celldifferentiation BG333618 CD74 CD74; MHC, class −12.3 2.90E−05 IIinvariant chain AW950965 CD3E CD3e; epsilon −1.5 9.80E−03 (CD3-TCRcomplex) M12824 CD8A CD8a molecule −3.9 1.20E−04 NM_001767 CD2 CD2molecule −1.3 3.40E−02 AA310902 CD3D CD3d; delta (CD3- −2.1 2.90E−03 TCRcomplex) XM_001772 LCK lymphocyte-specific −2 1.50E−04 protein tyrosinekinase NM_000616 CD4 CD4 molecule −2.3 1.00E−03 NM_003177 SYK Spleentyrosine −1.8 7.60E−03 kinase U81504 AP3B1 Adaptor-related −1.6 1.00E−02protein complex 3, beta 1 subunit NM_002838 PTPRC Protein tyrosine −3.22.50E−03 phosphatase, receptor type, C B cell differentiation AU118181KLF6 Kruppel-like factor 6 −2.6 3.70E−04 NM_000589 IL4 Interleukin 4−1.6 6.60E−02 NM_003177 SYK Spleen tyrosine −1.8 7.60E−03 kinase NK Tcell differentiation U81504 AP3B1 Adaptor-related −1.6 1.00E−02 proteincomplex 3, beta 1 subunit Monocyte differentiation BG434340 IFI16Interferon, gamma- −1.7 2.70E−03 inducible protein 16 NM_002473 MYH9Myosin, heavy chain −3.4 2.00E−05 9, non-muscle Myeloid celldifferentiation AA777633 MYST3 MYST histone −1.6 3.30E−03acetyltransferase (monocytic leukemia) 3 AL551154 HCLS1 Hematopoieticcell- −7 2.20E−06 specific Lyn substrate 1 AI739539 PF4 Platelet factor4 −3.3 1.10E−04 (chemokine (C-X-C motif) ligand 4) Y14768 TNFA TNF-alpha−1.3 9.90E−03 BG108304 LYN V-yes-1 Yamaguchi −3.2 4.50E−05 sarcoma viralrelated oncogene homolog XM_008993 SPIB Spi-B transcription −1.51.40E−03 factor (Spi-1/PU.1 related) AF177765 TLR4 toll-like receptor 4−1.8 9.60E−03 (TLR4) NM_000589 IL4 Interleukin 4 −1.6 6.60E−02 AA583143MAFB V-maf −2.7 1.00E−04 musculoaponeurotic fibrosarcoma oncogenehomolog B (avian) NM_006509 RELB V-rel −1.9 1.30E−04reticuloendotheliosis viral oncogene homolog B, nuclear factor of kappalight polypeptide gene enhancer in B-cells 3 (avian) AA253017 MYST1 MYSThistone −1.5 5.70E−02 acetyltransferase 1 T cell proliferation AW950965CD3E CD3e molecule, −1.5 9.80E−03 epsilon (CD3-TCR complex) NM_000887ITGAX Integrin, alpha X −1.4 2.10E−02 (complement component 3 receptor 4subunit) BC001257 GLMN Glomulin, FKBP −1.5 1.80E−02 associated proteinAI803460 CCND3 Cyclin D3 −1.5 8.80E−03 AC002310 ITGAL integrin, alpha 1−1.4 7.30E−02 (antigen CD11A (P180), lymphocyte function-associatedantigen 1; alpha polypeptide) NM_000589 IL4 Interleukin 4 −1.6 6.60E−02NM_003177 SYK Spleen tyrosine −1.8 7.60E−03 kinase NM_000632 ITGAMIntegrin, alpha M −2.3 6.10E−04 (complement component 3 receptor 3subunit) NM_002838 PTPRC Protein tyrosine −3.2 2.50E−03 phosphatase,receptor type, C AW780437 PRKCQ Protein kinase C, −1.7 9.10E−03 thetaAL136450 BCORL1 BCL6 co-repressor- −1.7 3.90E−04 like 1 B cellproliferation XM_003106 PRKCD protein kinase C, −1.9 8.80E−04 deltaNM_000589 IL4 Interleukin 4 −1.6 6.60E−02 NM_001250 CD40 CD40 molecule,−1.4 1.80E−02 TNF receptor superfamily member L26165 CDKN1ACyclin-dependent −3.8 2.90E−05 kinase inhibitor 1A (p21, Cip1) NM_002838PTPRC Protein tyrosine −3.2 2.50E−03 phosphatase, receptor type, Cactivated T cell proliferation NM_000887 ITGAX Integrin, alpha X −1.42.10E−02 (complement component 3 receptor 4 subunit) AC002310 ITGALintegrin, alpha 1 −1.4 7.30E−02 (antigen CD11A (P180), lymphocytefunction-associated antigen 1; alpha polypeptide) NM_000589 IL4Interleukin 4 −1.6 6.60E−02 NM_000632 ITGAM Integrin, alpha M −2.36.10E−04 (complement component 3 receptor 3 subunit) NK cellproliferation AL136450 BCORL1 BCL6 co-repressor- −1.7 3.90E−04 like 1microbial pattern recognition and binding AI739539 PF4 Platelet factor 4−3.3 1.10E−04 (CXCL4) AI097512 CHIT1 Chitinase 1 −1.5 2.00E−02(chitotriosidase) NM_003264 TLR2 Toll-like receptor 2 −2.6 1.00E−03AF177765 TLR4 toll-like receptor 4 −1.8 9.60E−03 (TLR4) XM_012649 SCYA7Small inducible −1.5 2.80E−02 cytokine A7 (monocyte chemotactic AL549182CD14 CD14 molecule −3.5 8.20E−06 NM_002620 PF4V1 Platelet factor 4 −2.71.90E−03 variant 1 AA188236 CLP1 CLP1, cleavage and −1.5 1.60E−02polyadenylation factor I subunit, homolog (S. cerevisiae) AI087056TICAM1 Toll-like receptor −1.5 3.30E−03 adaptor molecule 1 AF054013FPRL1 Formyl peptide −1.9 2.40E−03 receptor-like 1 L10820 FPR1 HumanN-formyl −1.8 3.10E−05 peptide receptor antigen processing andpresentation BG333618 CD74 CD74; MHC, class −12.3 2.90E−05 II invariantchain BF795929 HLA-DRA MHC, class II, DR −8.3 1.20E−05 alpha U83582HLA-DQB1 MHC, class II, DQ −2 5.20E−05 beta 1 AI634950 IGHG1 Ig heavyconstant −11.8 6.20E−08 gamma1 (G1m marker) AL571972 FCGRT Fc fragmentof IgG, −1.6 5.10E−02 receptor, transporter, alpha AV759427 HLA-DPA1MHC, class II, DP −6.8 2.70E−06 alpha 1 M83664 HLA-DPB1 MHC, class II,DP −2.8 6.30E−06 beta 1 AL561631 IFI30 Interferon, gamma- −2.6 2.80E−03inducible protein 30 NM_006674 MICA MHC class I −2.2 2.50E−03polypeptide-related sequence A BG327758 HLA-B MHC, class I, B — 2.70E−06AF071019 HLA-G HLA-G −2.4 2.60E−06 histocompatibility antigen, class I,G BF663123 IGHA1 Ig heavy constant −2.5 2.80E−03 alpha 1 AW407113 HLA-CMHC, class I, C −5.3 6.50E−07 BG176768 HLA-DOB MHC, class II, DO −2.41.60E−04 beta NM_006509 RELB Nuclear factor of −1.9 1.30E−04 kappa lightpolypeptide gene enhancer in B-cells 3 M20503 HLA-DRB1 MHC, class II, DR−11.8 5.30E−06 beta 1 U81504 AP3B1 Adaptor-related −1.6 1.00E−02 proteincomplex 3, beta 1 subunit AV710740 B2M Beta-2- −3.9 4.30E−08microglobulin cytokine activity XM_003506 PPBP pro-platelet basic −4.18.10E−05 protein (includes platelet basic AI739539 PF4 Platelet factor 4−3.3 1.10E−04 (chemokine (C-X-C motif) ligand 4) Y14768 TNFA TNF-alpha−1.3 9.90E−03 XM_003507 SCYB5 Small inducible −5.2 4.90E−05 cytokinesubfamily B (Cys-X-Cys), XM_005349 TNFSF8 tumor necrosis factor −1.91.50E−03 (ligand) superfamily, member 8 W38319 IL1B Interleukin 1, beta−6.3 2.70E−07 NM_002988 CCL18 Chemokine (C-C −1.6 1.20E−03 motif) ligand18 (pulmonary and activation-regulated) AV717082 IL8 Interleukin 8 —3.20E−04 BG108304 LYN V-yes-1 Yamaguchi −3.2 4.50E−05 sarcoma viralrelated oncogene homolog XM_012649 SCYA7 small inducible −1.5 2.80E−02cytokine A7 (monocyte chemotactic NM_000589 IL4 Interleukin 4 −1.66.60E−02 NM_000575 IL1A Interleukin 1, alpha −5.2 2.30E−05 XM_003508GRO3 GRO3 oncogene −1.5 2.10E−02 AA569974 CCL5 Chemokine (C-C −1.64.30E−03 motif) ligand 5 NM_005408 CCL13 Chemokine (C-C −1.6 1.90E−02motif) ligand 13 BG288796 IL1RN Interleukin 1 −3.6 3.90E−04 receptorantagonist AW188005 LTB Lymphotoxin beta −3.2 1.00E−03 (TNF superfamily,member 3) BC001257 GLMN Glomulin, FKBP −1.5 1.80E−02 associated proteinAW965098 CCL20 Chemokine (C-C −1.5 4.30E−03 motif) ligand 20 BG393056PRL Prolactin −1.5 1.40E−02 BG491425 CXCL1 Chemokine (C-X-C −15.26.90E−06 motif) ligand 1 (melanoma growth stimulating activity, alpha)NM_002620 PF4V1 Platelet factor 4 −2.7 1.90E−03 variant 1 cytokinebinding (receptors) AF009962 CCR-5 CC-chemokine −1.5 1.00E−02 receptor(CCR-5) NM_000877 IL1R1 Interleukin 1 −1.5 3.40E−02 receptor, type INM_000418 IL4R Interleukin 4 −1.6 9.40E−03 receptor XM_008651 CCR7Chemokine (C-C −17 4.30E−08 motif) receptor 7 NM_001558 IL10RAInterleukin 10 −1.6 1.20E−02 receptor, alpha NM_000878 IL2RB Interleukin2 −2.7 6.30E−06 receptor, beta AF012629 TNFRSF10C Tumor necrosis −1.72.30E−03 factor receptor superfamily, member 10c, decoy without anintracellular domain XM_001743 TNFRSF1B Tumor necrosis −2.4 1.80E−03factor receptor superfamily, member 1B BC001281 TNFRSF10B Tumor necrosis−1.5 3.60E−03 factor receptor superfamily, member 10b NM_001250 CD40CD40 molecule, −1.4 1.80E−02 TNF receptor superfamily member AL050337IFNGR1 interferon gamma −1.6 6.20E−03 receptor 1 AL550285 IFNGR2Interferon gamma −1.8 6.50E−03 receptor 2 (interferon gammatransducer 1) IL-12 biosynthesis NM_003998 NFKB1 Nuclear factor of −3.75.20E−05 kappa light polypeptide gene enhancer in B-cells 1 (p105)NM_002198 IRF1 Interferon regulatory −2.2 4.50E−04 factor 1 AF177765TLR4 toll-like receptor 4 −1.8 9.60E−03 (TLR4) IL-6 biosynthesis W39546CEBPB CCAAT/enhancer −1.9 5.30E−03 binding protein (C/EBP), beta W38319IL1B Interleukin 1, beta −6.3 2.70E−07 AF177765 TLR4 toll-like receptor4 −1.8 9.60E−03 (TLR4) IL-2 biosynthesis BC001257 GLMN Glomulin, FKBP−1.5 1.80E−02 associated protein NM_000616 CD4 CD4 molecule −2.31.00E−03 AW780437 PRKCQ Protein kinase C, −1.7 9.10E−03 theta IL-3biosynthesis NM_003177 SYK Spleen tyrosine −1.8 7.60E−03 kinase IL-1biosynthesis AF177765 TLR4 toll-like receptor 4 −1.8 9.60E−03 (TLR4)gene, inflammatory response AL570708 CD180 CD180 molecule −1.3 1.50E−02AL549182 CD14 CD14 molecule −3.5 8.20E−06 U08198 C8G Human complement−1.5 4.00E−03 C8 gamma subunit precursor (C8G) gene, complete cds.NM_003264 TLR2 Toll-like receptor 2 −2.6 1.00E−03 XM_006848 KRT1 keratin1 −2 1.70E−03 (epidermolytic hyperkeratosis) NM_001250 CD40 CD40molecule, −1.4 1.80E−02 TNF receptor superfamily member NM_004029 IRF7Interferon regulatory −2.2 5.60E−04 factor 7 W39546 CEBPB CCAAT/enhancer−1.9 5.30E−03 binding protein (C/EBP), beta X04011 CYBB Cytochromeb-245, −1.6 3.20E−03 beta polypeptide (chronic granulomatous disease)AF177765 TLR4 toll-like receptor 4 −1.8 9.60E−03 (TLR4) AI090294 CD97CD97 molecule −1.7 1.90E−04 NM_003998 NFKB1 Nuclear factor of −3.75.20E−05 kappa light polypeptide gene enhancer in B-cells 1 (p105)NM_000211 ITGB2 Integrin, beta 2 −2.2 6.30E−06 (complement component 3receptor 3 and 4 subunit) AC002310 ITGAL integrin, alpha 1 −1.4 7.30E−02(antigen CD11A (P180), lymphocyte function-associated antigen 1; alphapolypeptide) ID Name Description Fold P-value Cholesterol and othersteroids biosynthesis AL558223 ACBD3 Acyl-Coenzyme A 1.6 4.10E−03binding domain containing 3 BE253839 DHCR24 24- 2.1 1.60E−02dehydrocholesterol reductase AW271546 HSD17B1 Hydroxysteroid (17- 1.62.50E−03 beta) dehydrogenase 1 AF078850 HSD17B12 Hydroxysteroid (17- 1.41.70E−02 beta) dehydrogenase 12 AK001889 PRLR Prolactin receptor 1.95.00E−03 NM_000786 CYP51A1 Cytochrome P450, 1.9 3.90E−04 family 51,subfamily A, polypeptide 1 NM_004110 FDXR Ferredoxin reductase 1.85.00E−03 NM_000103 CYP19A1 Cytochrome P450, 1.9 1.60E−02 family 19,subfamily A, polypeptide 1 BE378962 DHCR7 7-dehydrocholesterol 1.82.60E−03 reductase J05158 CPN2 Carboxypeptidase N, 1.9 2.10E−03polypeptide 2, 83 kD AL521605 OPRS1 Opioid receptor, 2.2 4.70E−04 sigma1 AW117731 HMGCS1 3-hydroxy-3- 2.2 2.00E−03 methylglutaryl- Coenzyme Asynthase 1 (soluble) BG324529 MVD Mevalonate 2.3 5.20E−03 (diphospho)decarboxylase Ergosterol biosynthesis AL521605 OPRS1 Opioid receptor,2.2 4.70E−04 sigma 1 Dopamine biosynthesis AW156890 SNCA Synuclein,alpha 1.5 1.20E−02 (non A4 component of amyloid precursor) Fatty acidbiosynthesis AL359403 MCAT Malonyl CoA: ACP 1.6 5.40E−03 acyltransferase(mitochondrial) AF097514 SCD Stearoyl-CoA 5.2 4.40E−04 desaturase(delta-9- desaturase) transcription Transcription factors BE266904 SATB1Special AT-rich −4.2 1.70E−06 sequence binding protein 1 NM_006763 BTG2BTG family, −3.8 7.70E−04 member 2 NM_003998 NFKB1 NFk light −3.77.00E−05 polypeptide gene enhancer in B-cells 1 (p105) AI348005 BTG1B-cell translocation −3.4 3.70E−05 gene 1, anti- proliferative NM_006060IKZF1 IKAROS family zinc −2.6 7.00E−05 finger 1 (Ikaros) AL555297 SF1Splicing factor 1 −2.4 1.70E−06 NM_014795 ZFHX1B Zinc finger −2.36.10E−04 homeobox 1b AL561046 TSC22D3 TSC22 domain −2.2 5.00E−04 family,member 3 NM_002198 IRF1 Interferon regulatory −2.2 5.00E−04 factor 1NM_004029 IRF7 Interferon regulatory −2.2 5.80E−04 factor 7 AV708340UBA52 Ubiquitin A-52 −2.1 6.80E−04 residue ribosomal protein fusionproduct 1 AI631717 HNF4A Hepatocyte nuclear 2 3.90E−03 factor 4, alphaBG529476 HMGB2 High-mobility group 2.1 2.50E−03 box 2 BG340581 SREBF2Sterol regulatory 2.3 1.50E−03 element binding transcription factor 2AL525810 FOXM1 Forkhead box M1 2.3 2.40E−04 M95585 HLF Hepatic leukemia2.4 5.00E−04 factor NM_003220 TFAP2A Transcription factor 2.4 2.00E−03AP-2 alpha AL575644 NFKBIL1 NFk light 3.3 4.60E−03 polypeptide enhancerin B-cells inhibitor-like 1 Ssuperoxide metabolism BG035651 SOD2Superoxide −10.3 1.20E−07 dismutase 2, mitochondrial BG421245 CYBACytochrome b-245, −2.1 1.00E−06 alpha polypeptide XM_002200 NCF2neutrophil cytosolic −2 2.90E−04 factor 2 (65 kD, chronic heat Heatshock proteins BG327949 HSP90B1 Heat shock protein 1.6 4.50E−02 90 kDabeta (Grp94), member 1 AB007877 HSPA12A Heat shock 70 kDa 1.7 2.10E−03protein 12A BE742483 HSPA4 Heat shock 70 kDa 1.9 1.00E−05 protein 4AI640615 BAG4 BCL2-associated 1.9 1.10E−03 athanogene 4 BG032173 HSPD1Heat shock 60 kDa 2.5 5.90E−04 protein 1 (chaperonin)

Example 1

The biomarker findings are presented which were identified from geneexpression changes in leukocytes collected from (informed and consented)US Army Ranger Cadets who underwent eight-weeks of Army Ranger Training(RASP, Ranger Assessment and Selection Program). Our subjects wereexposed to extreme physical and psychological stressors of RangerTraining, which is designed to emulate extreme battlefield scenariossuch as strenuous physical activity, sleep deprivation, calorierestriction, and survival emotional stresses—pushing cadets to theirphysical and psychological limits. Though these men were among the bestof the best, many trainees dropped out in the first phase of thethree-phased RASP Training. The Army Ranger population provides a rareopportunity to study extreme stress, and to contribute to theunderstanding of intense chronic stress in general. Particularly, theability to collect pre-training samples for comparison withpost-training samples is rarely practical in any other chronically andextremely stressed patients.

Our studies focus in identifying molecular mediators of compromisedprotective immunity caused by social and battlefield-like stresses, andin identifying pathogen-induced biomarkers under severe stressbackground. Social and physiological stresses, particularly, which arefrequent or chronic are major contributors of stress-induced immunedysfunction. In this study, we employed experimental and computationalapproaches to identify molecules and signaling pathways involved in thehost's response towards battlefield-like stress, and in assessingprotective immunity status of the stressed host towards infection.

In the first approach, we used genome-wide transcriptome, and microRNAprofiling and in-vitro pathogen exposure of leukocytes (isolated fromArmy Ranger Trainees) to identify stress-suppressed transcripts andpathways critical in protective immune response. We have identified anumber of stress response biomarkers (transcripts and pathways) thathave potential implication in compromising the immune function. The mostcompromised pathways include antigen preparation and presentation, andT-cell activation pathways. Suppressed immune response genes remainedsuppressed even after ex-vivo exposure of post-RASP leukocytes to themitogenic toxin, Staphylococcal enterotoxin B (SEB). On the other hand,complete and differential counts of post-training WBCs were withinnormal ranges. This impaired activation is an indicator of anergy, andcompromised protective immunity.

Example 2

In the second approach, we used rigorous computational analyses inidentifying up-stream regulatory modules (and molecular networks) ofstress-suppressed genes. We identified up-stream regulators ofdifferentially altered transcripts, which include immune related andsteroid hormone inducible transcription factors, stress responsefactors, and microRNAs. Some stress induced microRNAs, and a number ofstress-inhibited transcription factors were found to regulate or bemodulated by many compromised immune response transcripts.

The identification of exceptionally enriched suppression of antigenpresentation and lymphocyte activation pathways (in spite of normalblood cell counts) are remarkable since these findings are consistentwith prior observations of poor vaccine responses, impaired woundhealing and infection susceptibility associated with chronic intensestress.

Some of the transcripts were unique to RASP stressors (severe andchronic stress), even in the presence of other pathogens, to which webriefly refer in this manuscript. These specific transcripts may havepotential use as diagnostic markers to distinguish debilitating chronicstress from that of infection.

CONCLUSION

The subject matter of the present invention (biomarkers) solves thedrawbacks of other routinely used assays that check the status of theimmune system process. Many clinical laboratories do differential andcomplete white blood cell counting to ascertain integrity of the immunesystem. Some advanced clinical laboratories do challenge assays(proliferation assays) to check the viability of immune cells (inaddition to cell counting). In our case, even though the cells arewithin their normal ranges (cell counting would have indicated normal),we still see no measurable response to SEB challenge (and we have themolecular indicators of the why). Our molecular markers can be used tocheck the protective or compromised nature of the immune systemregardless of whether the cells are anergic (within normal range interms of their numbers but not protective) or otherwise.

DEFINITIONS

Welch's t-test: Statistical comparative analysis whereby the means andvariance of compared groups are not assumed to be the equal.

Transcriptome: Genome-wide transcripts of human or any other livingthing.Transcript: Messenger RNA (ribonucleic acid) or any other small RNAmolecule.Pathway: regulatory hierarchy of bio-molecules (proteins, transcripts,or metabolites) forming a specific biological process (function).Normal Control: A person or sample from a person, or genes ortranscripts from a person, or expression profile from a person orpersons that has not been subjected to stress.Diagnostic biomarkers: stress effected genes, transcripts, cDNAs, mRNA,miRNAs, rRNA, tRNA, peptides and proteins.**Gene names and accession numbers presented herein are standard genenames and accession numbers for genes that are found in the NCBI GenBankS. GenBank® is the NIH genetic sequence database, an annotatedcollection of all publicly available DNA sequences (Nucleic AcidsResearch, 2013 January; 41(D1):D36-42). GenBank is part of theInternational Nucleotide Sequence Database Collaboration, whichcomprises the DNA DataBank of Japan (DDBJ), the European MolecularBiology Laboratory (EMBL), and GenBank at NCBI. These threeorganizations exchange data on a daily basis.

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What is claimed is:
 1. A set of isolated diagnostic biomarkers fordiagnosing immune suppression/dysfunction, wherein said diagnosticbiomarkers are genes or transcripts that have expression that areup-regulated or down-regulated upon stress when compared to a normalcontrol.
 2. The set of isolated diagnostic biomarkers of claim 1,wherein said biomarkers comprise the at least 5 or more of the genes:CCR7, IGHG1, CSPG2, LAPTM5, CSF1R, ALB, HLA-C, HLA-DRA, HLA-DPA1, CD14,LOC652128, MGAT1, HCLS1, ANPEP, IL1B, IL1B, SATB1, LCP1, AQP9, HLA-DRB1,NP, AFP, DUSP6, B2M, SCYB5, FCN1, FTH1, HLA-DRB1, PPBP, FCGR3A, IGHG1,IGFBP1, WIPF1, MAGEA6, LPXN, CXCL1, GAGE2, CDKN1A, FCGR3A, TTC9, FYN,SERPINB2, CENPF, LIMS1, MDK, AX025098, A2M, CD74, IER3, HLA-B, ACTB,ANXA1, LAIR1, CD44, COL6A1, PRKCH, MAFB, EVI2A, LAT.
 3. The set ofisolated diagnostic biomarkers of claim 1, wherein said suppressedimmune response includes suppressed inflammatory response, suppressedleukocyte activations and proliferations, and/or suppressed response topathogens.
 4. The set of isolated diagnostic biomarkers of claim 1,wherein said diagnostic biomarkers comprises at least five ofdifferentially regulated genes or transcripts comprising: CCR7, IGHG1,CSPG2, LAPTM5, CSF1R.
 5. The set of isolated diagnostic biomarkers ofclaim 1, wherein said diagnostic biomarker comprises at least sevendifferentially regulated genes or transcripts comprising: CCR7, IGHG1,CSPG2, LAPTM5, CSF1R, ALB, HLA-C.
 6. The diagnostic biomarkers of claim1, wherein said diagnostic biomarkers comprise at least 10differentially regulated genes or transcripts comprising: CCR7, IGHG1,CSPG2, LAPTM5, CSF1R, ALB, HLA-C, HLA-DRA, HLA-DPA1, CD14.
 7. The set ofisolated diagnostic biomarkers of claim 1, wherein said diagnosticbiomarkers comprise at least 20 differentially regulated genes ortranscripts comprising: CCR7, IGHG1, CSPG2, LAPTM5, CSF1R, ALB, HLA-C,HLA-DRA, HLA-DPA1, CD14, LOC652128, MGAT1, HCLS1, ANPEP, IL1B, IL1B,SATB1, LCP1, AQP9, HLA-DRB1.
 8. A library of differentially regulatedtranscripts or genes from their corresponding pathway, wherein saiddifferently regulated transcripts or genes from their correspondingpathway are suitable for use as diagnostic biomarkers for diagnosingsuppressed immune response in a subject, wherein said suppressed immuneresponse is due to stress.
 9. The library of claim 8, wherein saidsuppressed immune response includes suppressed inflammatory response,suppressed leukocyte activation and proliferations, and/or suppressedresponse to pathogens.
 10. A micro- or nano-chip, or PCR for observingdifferentially regulated genes or transcripts wherein said micro- ornano-chip comprises the genes: CCR7, IGHG1, CSPG2, LAPTM5, CSF1R, ALB,HLA-C, HLA-DRA, HLA-DPA1, CD14, LOC652128, MGAT1, HCLS1, ANPEP, IL1B,IL1B, SATB1, LCP1, AQP9, HLA-DRB1, NP, AFP, DUSP6, B2M, SCYB5, FCN1,FTH1, HLA-DRB1, PPBP, FCGR3A, IGHG1, IGFBP1, WIPF1, MAGEA6, LPXN, CXCL1,GAGE2, CDKN1A, FCGR3A, TTC9, FYN, SERPINB2, CENPF, LIMS1, MDK, AX025098,A2M, CD74, IER3, HLA-B, ACTB, ANXA1, LAIR1, CD44, COL6A1, PRKCH, MAFB,EVI2A, LAT, and said PCR uses cDNAs of said differentially regulatedgenes or transcripts.
 11. The micro- or nano-chip of claim 10, whereinsaid cDNAs are electrochemically tethered in the wells of said micro- ornano-chip.
 12. A diagnostic kit for use in screening integrity of theimmune function of a subject, wherein said diagnostic kit comprises aset of isolated diagnostic biomarkers of a claim
 1. 13. The diagnostickit of claim 12, wherein said diagnostic biomarkers are present in/on amicro- or nano-chip.
 14. The diagnostic kit of claim 13, wherein saiddiagnostic biomarkers are electrochemically tethered in the wells ofsaid micro- or nano-chip.
 15. A method of evaluating immune function ordisfunction in a patient, comprising the steps of a) creating a libraryof leukocyte diagnostic biomarkers that are up-regulated ordown-regulated in response to stress; comparing a set of diagnosticmarkers in said patient to said diagnostic markers in said library; anddetermining whether said patient is under stress.
 16. The method ofclaim 15, wherein said diagnostic biomarkers are selected from thefollowing genes: CCR7, IGHG1, CSPG2, LAPTM5, CSF1R, ALB, HLA-C, HLA-DRA,HLA-DPA1, CD14, LOC652128, MGAT1, HCLS1, ANPEP, IL1B, IL1B, SATB1, LCP1,AQP9, HLA-DRB1, NP, AFP, DUSP6, B2M, SCYB5, FCN1, FTH1, HLA-DRB1, PPBP,FCGR3A, IGHG1, IGFBP1, WIPF1, MAGEA6, LPXN, CXCL1, GAGE2, CDKN1A,FCGR3A, TTC9, FYN, SERPINB2, CENPF, LIMS1, MDK, AX025098, A2M, CD74,IER3, HLA-B, ACTB, ANXA1, LAIR1, CD44, COL6A1, PRKCH, MAFB, EVI2A, LAT.17. The method of claim 15, wherein said diagnostic biomarkers are atleast 5 of the following genes: CCR7, IGHG1, CSPG2, LAPTM5, CSF1R, ALB,HLA-C, HLA-DRA, HLA-DPA1, CD14, LOC652128, MGAT1, HCLS1, ANPEP, IL1B,IL1B, SATB1, LCP1, AQP9, HLA-DRB1, NP, AFP, DUSP6, B2M, SCYB5, FCN1,FTH1, HLA-DRB1, PPBP, FCGR3A, IGHG1, IGFBP1, WIPF1, MAGEA6, LPXN, CXCL1,GAGE2, CDKN1A, FCGR3A, TTC9, FYN, SERPINB2, CENPF, LIMS1, MDK, AX025098,A2M, CD74, IER3, HLA-B, ACTB, ANXA1, LAIR1, CD44, COL6A1, PRKCH, MAFB,EVI2A, LAT.
 18. A method of screening a subject for immune suppressionor dysfunction, comprising: taking a sample of diagnostic biomarkersfrom said subject; comparing said patient's diagnostic markers to normaldiagnostic markers in a control library; and determining whether saidpatient has immune suppression or dysfunction.
 19. A method ofidentifying the capability of immune cells to respond to pathogenicagents without exposing the subjects or their cells to any pathogens,comprising obtaining a sample from said subject and comparing diagnosticbiomarkers in said sample to a biomarker stress profile, said biomarkerstress profile comprising a pre-determined set of transcripts thatindicate stress.
 20. The method of claim 19, wherein said set includes 5or more of the following transcripts: CCR7, IGHG1, CSPG2, LAPTM5, CSF1R,ALB, HLA-C, HLA-DRA, HLA-DPA1, CD14, LOC652128, MGAT1, HCLS1, ANPEP,IL1B, IL1B, SATB1, LCP1, AQP9, HLA-DRB1, NP, AFP, DUSP6, B2M, SCYB5,FCN1, FTH1, HLA-DRB1, PPBP, FCGR3A, IGHG1, IGFBP1, WIPF1, MAGEA6, LPXN,CXCL1, GAGE2, CDKN1A, FCGR3A, TTC9, FYN, SERPINB2, CENPF, LIMS1, MDK,AX025098, A2M, CD74, IER3, HLA-B, ACTB, ANXA1, LAIR1, CD44, COL6A1,PRKCH, MAFB, EVI2A, LAT.
 21. The set of isolated diagnostic biomarkersfor diagnosing immune suppression/dysfunction of claim 1, wherein saiddiagnostic biomarkers are cDNAs of said differentially regulated genesor transcripts.
 22. The set of isolated diagnostic biomarkers fordiagnosing immune suppression/dysfunction of claim 1, wherein saiddiagnostic biomarkers are RNAs of said differentially regulated genes ortranscripts.
 23. The set of isolated diagnostic biomarkers fordiagnosing immune suppression/dysfunction of claim 1, wherein saidbiomarkers are associated with microbial pattern recognition,inflammation, cytokine production and reception, adhesion, immunologicalsynapse formation, regulation of immune response, chemotaxis, antigenpresentation and activations of lymphocytes, activation of myloidlymphocytes, activation of mast cells and activation of macrophages. 24.The set of isolated diagnostic biomarkers for diagnosing immunesuppression/dysfunction of claim 1, wherein said set includestranscription factors and stress response factors.
 25. The peptides orproteins encoded by the genes of claim
 1. 26. A method of diagnosingexposure to SEB, dengue virus and/or yersinia pestis in a patient,comprising: a) creating a library of stress specific genes ortranscripts differentiating stress in leukocytes from subjects exposedto SEB, dengue virus and Yersinia pestis; b) taking a sample from saidpatient; c) comparing genes or transcripts from said sample to saidlibrary; c) determining whether said patient has been exposed to SEB,dengue virus, or yersinia pestis based on the comparison of said genesfrom said sample to said library.